for Journals by Title or ISSN
for Articles by Keywords
help
  Subjects -> COMPUTER SCIENCE (Total: 2011 journals)
    - ANIMATION AND SIMULATION (30 journals)
    - ARTIFICIAL INTELLIGENCE (98 journals)
    - AUTOMATION AND ROBOTICS (98 journals)
    - CLOUD COMPUTING AND NETWORKS (61 journals)
    - COMPUTER ARCHITECTURE (9 journals)
    - COMPUTER ENGINEERING (9 journals)
    - COMPUTER GAMES (16 journals)
    - COMPUTER PROGRAMMING (24 journals)
    - COMPUTER SCIENCE (1172 journals)
    - COMPUTER SECURITY (46 journals)
    - DATA BASE MANAGEMENT (13 journals)
    - DATA MINING (32 journals)
    - E-BUSINESS (22 journals)
    - E-LEARNING (29 journals)
    - ELECTRONIC DATA PROCESSING (21 journals)
    - IMAGE AND VIDEO PROCESSING (39 journals)
    - INFORMATION SYSTEMS (108 journals)
    - INTERNET (92 journals)
    - SOCIAL WEB (50 journals)
    - SOFTWARE (34 journals)
    - THEORY OF COMPUTING (8 journals)

COMPUTER SCIENCE (1172 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 15)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 24)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 9)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 15)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 6)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 2)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 21)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 9)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 25)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 26)
Advanced Science Letters     Full-text available via subscription   (Followers: 9)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 16)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 6)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 18)
Advances in Computer Science : an International Journal     Open Access   (Followers: 15)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 11)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 27)
Advances in Human-Computer Interaction     Open Access   (Followers: 21)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 40)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 4)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 8)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 9)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 5)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 11)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 13)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 15)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 135)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 4)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 11)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Big Data and Cognitive Computing     Open Access  
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 285)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 14)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 18)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 34)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 47)
British Journal of Educational Technology     Hybrid Journal   (Followers: 137)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 2)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 14)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 1)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 14)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 20)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 55)
Communications of the Association for Information Systems     Open Access   (Followers: 18)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 11)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 15)
Computational Linguistics     Open Access   (Followers: 22)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 91)
Computer Aided Surgery     Hybrid Journal   (Followers: 5)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 9)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 12)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 2)
Computer Music Journal     Hybrid Journal   (Followers: 18)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 8)
Computer Science and Engineering     Open Access   (Followers: 19)
Computer Science and Information Technology     Open Access   (Followers: 13)

        1 2 3 4 5 6 | Last

Journal Cover Advances in Data Analysis and Classification
  [SJR: 1.113]   [H-I: 14]   [52 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1862-5355 - ISSN (Online) 1862-5347
   Published by Springer-Verlag Homepage  [2355 journals]
  • Robust clustering for functional data based on trimming and constraints
    • Abstract: Many clustering algorithms when the data are curves or functions have been recently proposed. However, the presence of contamination in the sample of curves can influence the performance of most of them. In this work we propose a robust, model-based clustering method that relies on an approximation to the “density function” for functional data. The robustness follows from the joint application of data-driven trimming, for reducing the effect of contaminated observations, and constraints on the variances, for avoiding spurious clusters in the solution. The algorithm is designed to perform clustering and outlier detection simultaneously by maximizing a trimmed “pseudo” likelihood. The proposed method has been evaluated and compared with other existing methods through a simulation study. Better performance for the proposed methodology is shown when a fraction of contaminating curves is added to a non-contaminated sample. Finally, an application to a real data set that has been previously considered in the literature is given.
      PubDate: 2018-02-03
      DOI: 10.1007/s11634-018-0312-7
       
  • Methods for the analysis of asymmetric pairwise relationships
    • Authors: Giuseppe Bove; Akinori Okada
      Abstract: Asymmetric pairwise relationships are frequently observed in experimental and non-experimental studies. They can be analysed with different aims and approaches. A brief review of models and methods of multidimensional scaling and cluster analysis able to deal with asymmetric proximities is provided taking a ‘data-analytic’ approach and emphasizing data visualization.
      PubDate: 2018-02-01
      DOI: 10.1007/s11634-017-0307-9
       
  • A method for selecting the relevant dimensions for high-dimensional
           classification in singular vector spaces
    • Authors: Dawit G. Tadesse; Mark Carpenter
      Abstract: In this paper, we give a new feature selection algorithm for the binary class classification problem in sparse high-dimensional spaces. Singular value decomposition (SVD) is a popular dimension reduction method in higher-dimensional classification. The traditional SVD method begins by ranking the Singular Dimensions (SDs) from largest singular value to the smallest. However, when the number of signals is fewer than the number of noise, the first few ranked SDs are not necessarily the best for classification. We demonstrate, theoretically and empirically, that our method efficiently selects the SDs most appropriate for classification and significantly reduces the misclassification error. We also apply our method to real data text mining applications.
      PubDate: 2018-01-25
      DOI: 10.1007/s11634-018-0311-8
       
  • Comparisons among several methods for handling missing data in principal
           component analysis (PCA)
    • Authors: Sébastien Loisel; Yoshio Takane
      Abstract: Missing data are prevalent in many data analytic situations. Those in which principal component analysis (PCA) is applied are no exceptions. The performance of five methods for handling missing data in PCA is investigated, the missing data passive method, the weighted low rank approximation (WLRA) method, the regularized PCA (RPCA) method, the trimmed scores regression method, and the data augmentation (DA) method. Three complete data sets of varying sizes were selected, in which missing data were created randomly and non-randomly. These data were then analyzed by the five methods, and their parameter recovery capability, as measured by the mean congruence coefficient between loadings obtained from full and missing data, is compared as functions of the number of extracted components (dimensionality) and the proportion of missing data (censor rate). For randomly censored data, all five methods worked well when the dimensionality and censor rate were small. Their performance deteriorated, as the dimensionality and censor rate increased, but the speed of deterioration was distinctly faster with the WLRA method. The RPCA method worked best and the DA method came as a close second in terms of parameter recovery. However, the latter, as implemented here, was found to be extremely time-consuming. For non-randomly censored data, the recovery was also affected by the degree of non-randomness in censoring processes. Again the RPCA method worked best, maintaining good to excellent recoveries when the censor rate was small and the dimensionality of solutions was not too excessive.
      PubDate: 2018-01-18
      DOI: 10.1007/s11634-018-0310-9
       
  • New distance measures for classifying X-ray astronomy data into stellar
           classes
    • Authors: Amparo Baíllo; Javier Cárcamo; Konstantin Getman
      Abstract: The classification of the X-ray sources into classes (such as extragalactic sources, background stars,...) is an essential task in astronomy. Typically, one of the classes corresponds to extragalactic radiation, whose photon emission behaviour is well characterized by a homogeneous Poisson process. We propose to use normalized versions of the Wasserstein and Zolotarev distances to quantify the deviation of the distribution of photon interarrival times from the exponential class. Our main motivation is the analysis of a massive dataset from X-ray astronomy obtained by the Chandra Orion Ultradeep Project (COUP). This project yielded a large catalog of 1616 X-ray cosmic sources in the Orion Nebula region, with their series of photon arrival times and associated energies. We consider the plug-in estimators of these metrics, determine their asymptotic distributions, and illustrate their finite-sample performance with a Monte Carlo study. We estimate these metrics for each COUP source from three different classes. We conclude that our proposal provides a striking amount of information on the nature of the photon emitting sources. Further, these variables have the ability to identify X-ray sources wrongly catalogued before. As an appealing conclusion, we show that some sources, previously classified as extragalactic emissions, have a much higher probability of being young stars in Orion Nebula.
      PubDate: 2018-01-18
      DOI: 10.1007/s11634-018-0309-2
       
  • Mixture model modal clustering
    • Authors: José E. Chacón
      Abstract: The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high density separated from each other by zones of lower density, so that they are closely related to certain regions around the density modes. If the true density is indeed in the assumed class of mixture densities, then mixture model clustering allows to scrutinize more subtle situations than modal clustering. However, when mixture modeling is used in a nonparametric way, taking advantage of the denseness of the sieve of mixture densities to approximate any density, then the correspondence between clusters and mixture components may become questionable. In this paper we introduce two methods to adopt a modal clustering point of view after a mixture model fit. Examples are provided to illustrate that mixture modeling can also be used for clustering in a nonparametric sense, as long as clusters are understood as the domains of attraction of the density modes. Finally, a simulation study reveals that the new methods are extremely efficient from a computational point of view, while at the same time they retain a high level of accuracy.
      PubDate: 2018-01-13
      DOI: 10.1007/s11634-018-0308-3
       
  • Fuzzy data analysis and classification
    • Authors: Pierpaolo D’Urso; María Ángeles Gil
      Pages: 645 - 657
      PubDate: 2017-12-01
      DOI: 10.1007/s11634-017-0304-z
      Issue No: Vol. 11, No. 4 (2017)
       
  • A fuzzy approach to robust regression clustering
    • Authors: Francesco Dotto; Alessio Farcomeni; Luis Angel García-Escudero; Agustín Mayo-Iscar
      Pages: 691 - 710
      Abstract: A new robust fuzzy regression clustering method is proposed. We estimate coefficients of a linear regression model in each unknown cluster. Our method aims to achieve robustness by trimming a fixed proportion of observations. Assignments to clusters are fuzzy: observations contribute to estimates in more than one single cluster. We describe general criteria for tuning the method. The proposed method seems to be robust with respect to different types of contamination.
      PubDate: 2017-12-01
      DOI: 10.1007/s11634-016-0271-9
      Issue No: Vol. 11, No. 4 (2017)
       
  • Fuzzy rule based classification systems for big data with MapReduce:
           granularity analysis
    • Authors: Alberto Fernández; Sara del Río; Abdullah Bawakid; Francisco Herrera
      Pages: 711 - 730
      Abstract: Due to the vast amount of information available nowadays, and the advantages related to the processing of this data, the topics of big data and data science have acquired a great importance in the current research. Big data applications are mainly about scalability, which can be achieved via the MapReduce programming model.It is designed to divide the data into several chunks or groups that are processed in parallel, and whose result is “assembled” to provide a single solution. Among different classification paradigms adapted to this new framework, fuzzy rule based classification systems have shown interesting results with a MapReduce approach for big data. It is well known that the performance of these types of systems has a strong dependence on the selection of a good granularity level for the Data Base. However, in the context of MapReduce this parameter is even harder to determine as it can be also related with the number of Maps chosen for the processing stage. In this paper, we aim at analyzing the interrelation between the number of labels of the fuzzy variables and the scarcity of the data due to the data sampling in MapReduce. Specifically, we consider that as the partitioning of the initial instance set grows, the level of granularity necessary to achieve a good performance also becomes higher. The experimental results, carried out for several Big Data problems, and using the Chi-FRBCS-BigData algorithms, support our claims.
      PubDate: 2017-12-01
      DOI: 10.1007/s11634-016-0260-z
      Issue No: Vol. 11, No. 4 (2017)
       
  • Robust scale estimators for fuzzy data
    • Authors: Sara de la Rosa de Sáa; María Asunción Lubiano; Beatriz Sinova; Peter Filzmoser
      Pages: 731 - 758
      Abstract: Observations distant from the majority or deviating from the general pattern often appear in datasets. Classical estimates such as the sample mean or the sample variance can be substantially affected by these observations (outliers). Even a single outlier can have huge distorting influence. However, when one deals with real-valued data there exist robust measures/estimates of location and scale (dispersion) which reduce the influence of these atypical values and provide approximately the same results as the classical estimates applied to the typical data without outliers. In real-life, data to be analyzed and interpreted are not always precisely defined and they cannot be properly expressed by using a numerical scale of measurement. Frequently, some of these imprecise data could be suitably described and modelled by considering a fuzzy rating scale of measurement. In this paper, several well-known scale (dispersion) estimators in the real-valued case are extended for random fuzzy numbers (i.e., random mechanisms generating fuzzy-valued data), and some of their properties as estimators for dispersion are examined. Furthermore, their robust behaviour is analyzed using two powerful tools, namely, the finite sample breakdown point and the sensitivity curves. Simulations, including empirical bias curves, are performed to complete the study.
      PubDate: 2017-12-01
      DOI: 10.1007/s11634-015-0210-1
      Issue No: Vol. 11, No. 4 (2017)
       
  • A novel method for forecasting time series based on fuzzy logic and
           visibility graph
    • Authors: Rong Zhang; Baabak Ashuri; Yong Deng
      Pages: 759 - 783
      Abstract: Time series attracts much attention for its remarkable forecasting potential. This paper discusses how fuzzy logic improves accuracy when forecasting time series using visibility graph and presents a novel method to make more accurate predictions. In the proposed method, historical data is firstly converted into a visibility graph. Then, the strategy of link prediction is utilized to preliminarily forecast the future data. Eventually, the future data is revised based on fuzzy logic. To demonstrate the performance, the proposed method is applied to forecast Construction Cost Index, Taiwan Stock Index and student enrollments. The results show that fuzzy logic is able to improve the accuracy by designing appropriate fuzzy rules. In addition, through comparison, it is proved that our method has high flexibility and predictability. It is expected that our work will not only make contributions to the theoretical study of time series forecasting, but also be beneficial to practical areas such as economy and engineering by providing more accurate predictions.
      PubDate: 2017-12-01
      DOI: 10.1007/s11634-017-0300-3
      Issue No: Vol. 11, No. 4 (2017)
       
  • On ill-conceived initialization in archetypal analysis
    • Authors: Abdul Suleman
      Pages: 785 - 808
      Abstract: We show that an improper initialization of the matrix of prototypes, \({\mathbf {V}}\) , can be misleading, and potentially gives rise to a degenerate fuzzy partition when performing fuzzy clustering by means of an archetypal analysis. Subsequently, we propose an algorithm to correct the initial guess for \({\mathbf {V}}\) , which is grounded in two theoretical results on convex hulls. A numerical experiment carried out to assess its accuracy, and involving more than 200,000 initializations, shows a failure rate of below 0.8%.
      PubDate: 2017-12-01
      DOI: 10.1007/s11634-017-0303-0
      Issue No: Vol. 11, No. 4 (2017)
       
  • Weighted distance-based trees for ranking data
    • Authors: Antonella Plaia; Mariangela Sciandra
      Abstract: Within the framework of preference rankings, the interest can lie in finding which predictors and which interactions are able to explain the observed preference structures, because preference decisions will usually depend on the characteristics of both the judges and the objects being judged. This work proposes the use of a univariate decision tree for ranking data based on the weighted distances for complete and incomplete rankings, and considers the area under the ROC curve both for pruning and model assessment. Two real and well-known datasets, the SUSHI preference data and the University ranking data, are used to display the performance of the methodology.
      PubDate: 2017-12-16
      DOI: 10.1007/s11634-017-0306-x
       
  • Outlier detection in interval data
    • Authors: A. Pedro Duarte Silva; Peter Filzmoser; Paula Brito
      Abstract: A multivariate outlier detection method for interval data is proposed that makes use of a parametric approach to model the interval data. The trimmed maximum likelihood principle is adapted in order to robustly estimate the model parameters. A simulation study demonstrates the usefulness of the robust estimates for outlier detection, and new diagnostic plots allow gaining deeper insight into the structure of real world interval data.
      PubDate: 2017-12-15
      DOI: 10.1007/s11634-017-0305-y
       
  • Parametric classification with soft labels using the evidential EM
           algorithm: linear discriminant analysis versus logistic regression
    • Authors: Benjamin Quost; Thierry Denœux; Shoumei Li
      Abstract: Partially supervised learning extends both supervised and unsupervised learning, by considering situations in which only partial information about the response variable is available. In this paper, we consider partially supervised classification and we assume the learning instances to be labeled by Dempster–Shafer mass functions, called soft labels. Linear discriminant analysis and logistic regression are considered as special cases of generative and discriminative parametric models. We show that the evidential EM algorithm can be particularized to fit the parameters in each of these models. We describe experimental results with simulated data sets as well as with two real applications: K-complex detection in sleep EEGs signals and facial expression recognition. These results confirm the interest of using soft labels for classification as compared to potentially erroneous crisp labels, when the true class membership is partially unknown or ill-defined.
      PubDate: 2017-11-11
      DOI: 10.1007/s11634-017-0301-2
       
  • Non-symmetrical composite-based path modeling
    • Authors: Pasquale Dolce; Vincenzo Esposito Vinzi; Natale Carlo Lauro
      Abstract: Partial least squares path modeling presents some inconsistencies in terms of coherence with the predictive directions specified in the inner model (i.e. the path directions), because the directions of the links in the inner model are not taken into account in the iterative algorithm. In fact, the procedure amplifies interdependence among blocks and fails to distinguish between dependent and explanatory blocks. The method proposed in this paper takes into account and respects the specified path directions, with the aim of improving the predictive ability of the model and to maintain the hypothesized theoretical inner model. To highlight its properties, the proposed method is compared to the classical PLS path modeling in terms of explained variability, predictive relevance and interpretation using artificial data through a real data application. A further development of the method allows to treat multi-dimensional blocks in composite-based path modeling.
      PubDate: 2017-11-10
      DOI: 10.1007/s11634-017-0302-1
       
  • Clusterwise analysis for multiblock component methods
    • Authors: Stéphanie Bougeard; Hervé Abdi; Gilbert Saporta; Ndèye Niang
      Abstract: Multiblock component methods are applied to data sets for which several blocks of variables are measured on a same set of observations with the goal to analyze the relationships between these blocks of variables. In this article, we focus on multiblock component methods that integrate the information found in several blocks of explanatory variables in order to describe and explain one set of dependent variables. In the following, multiblock PLS and multiblock redundancy analysis are chosen, as particular cases of multiblock component methods when one set of variables is explained by a set of predictor variables that is organized into blocks. Because these multiblock techniques assume that the observations come from a homogeneous population they will provide suboptimal results when the observations actually come from different populations. A strategy to palliate this problem—presented in this article—is to use a technique such as clusterwise regression in order to identify homogeneous clusters of observations. This approach creates two new methods that provide clusters that have their own sets of regression coefficients. This combination of clustering and regression improves the overall quality of the prediction and facilitates the interpretation. In addition, the minimization of a well-defined criterion—by means of a sequential algorithm—ensures that the algorithm converges monotonously. Finally, the proposed method is distribution-free and can be used when the explanatory variables outnumber the observations within clusters. The proposed clusterwise multiblock methods are illustrated with of a simulation study and a (simulated) example from marketing.
      PubDate: 2017-11-08
      DOI: 10.1007/s11634-017-0296-8
       
  • Hierarchical clustering of asymmetric networks
    • Authors: Gunnar Carlsson; Facundo Mémoli; Alejandro Ribeiro; Santiago Segarra
      Abstract: This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods that, based on the dissimilarity structure, output hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter. Our construction of hierarchical clustering methods is built around the concept of admissible methods, which are those that abide by the axioms of value—nodes in a network with two nodes are clustered together at the maximum of the two dissimilarities between them—and transformation—when dissimilarities are reduced, the network may become more clustered but not less. Two particular methods, termed reciprocal and nonreciprocal clustering, are shown to provide upper and lower bounds in the space of admissible methods. Furthermore, alternative clustering methodologies and axioms are considered. In particular, modifying the axiom of value such that clustering in two-node networks occurs at the minimum of the two dissimilarities entails the existence of a unique admissible clustering method. Finally, the developed clustering methods are implemented to analyze the internal migration in the United States.
      PubDate: 2017-11-08
      DOI: 10.1007/s11634-017-0299-5
       
  • Clustering of imbalanced high-dimensional media data
    • Authors: Šárka Brodinová; Maia Zaharieva; Peter Filzmoser; Thomas Ortner; Christian Breiteneder
      Abstract: Media content in large repositories usually exhibits multiple groups of strongly varying sizes. Media of potential interest often form notably smaller groups. Such media groups differ so much from the remaining data that it may be worthy to look at them in more detail. In contrast, media with popular content appear in larger groups. Identifying groups of varying sizes is addressed by clustering of imbalanced data. Clustering highly imbalanced media groups is additionally challenged by the high dimensionality of the underlying features. In this paper, we present the imbalanced clustering (IClust) algorithm designed to reveal group structures in high-dimensional media data. IClust employs an existing clustering method in order to find an initial set of a large number of potentially highly pure clusters which are then successively merged. The main advantage of IClust is that the number of clusters does not have to be pre-specified and that no specific assumptions about the cluster or data characteristics need to be made. Experiments on real-world media data demonstrate that in comparison to existing methods, IClust is able to better identify media groups, especially groups of small sizes.
      PubDate: 2017-11-07
      DOI: 10.1007/s11634-017-0292-z
       
  • Rethinking an ROC partial area index for evaluating the classification
           performance at a high specificity range
    • Authors: Juana-María Vivo; Manuel Franco; Donatella Vicari
      Abstract: The area under a receiver operating characteristic (ROC) curve is valuable for evaluating the classification performance described by the entire ROC curve in many fields including decision making and medical diagnosis. However, this can be misleading when clinical tasks demand a restricted specificity range. The partial area under a portion of the ROC curve ( \({ pAUC}\) ) has more practical relevance in such situations, but it is usually transformed to overcome some drawbacks and improve its interpretation. The standardized \({ pAUC}\) ( \({ SpAUC}\) ) index is considered as a meaningful relative measure of predictive accuracy. Nevertheless, this \({ SpAUC}\) index might still show some limitations due to ROC curves crossing the diagonal line, and to the problem when comparing two tests with crossing ROC curves in the same restricted specificity range. This paper provides an alternative \({ pAUC}\) index which overcomes these limitations. Tighter bounds for the \({ pAUC}\) of an ROC curve are derived, and then a modified \({ pAUC}\) index for any restricted specificity range is established. In addition, the proposed tighter partial area index ( \({ TpAUC}\) ) is also shown for classifier when high specificity must be clinically maintained. The variance of the \({ TpAUC}\) is also studied analytically and by simulation studies in a theoretical framework based on the most typical assumption of a binormal model, and estimated by using nonparametric bootstrap resampling in the empirical examples. Simulated and real datasets illustrate the practical utility of the \({ TpAUC}\) .
      PubDate: 2017-10-27
      DOI: 10.1007/s11634-017-0295-9
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.90.202.184
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-