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  • Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals
    J. Classif. (IF 1.156) Pub Date : 2020-08-07
    José E. Chacón

    The problem of maximizing (or minimizing) the agreement between clusterings, subject to given marginals, can be formally posed under a common framework for several agreement measures. Until now, it was possible to find its solution only through numerical algorithms. Here, an explicit solution is shown for the case where the two clusterings have two clusters each.

    更新日期:2020-08-08
  • Initializing k -means Clustering by Bootstrap and Data Depth
    J. Classif. (IF 1.156) Pub Date : 2020-07-24
    Aurora Torrente, Juan Romo

    The k-means algorithm is widely used in various research fields because of its fast convergence to the cost function minima; however, it frequently gets stuck in local optima as it is sensitive to initial conditions. This paper explores a simple, computationally feasible method, which provides k-means with a set of initial seeds to cluster datasets of arbitrary dimensions. Our technique consists of

    更新日期:2020-07-24
  • Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering
    J. Classif. (IF 1.156) Pub Date : 2020-07-11
    Christophe Biernacki, Matthieu Marbac, Vincent Vandewalle

    A generic method is introduced to visualize in a “Gaussian-like way,” and onto \(\mathbb {R}^{2}\), results of Gaussian or non-Gaussian–based clustering. The key point is to explicitly force a visualization based on a spherical Gaussian mixture to inherit from the within cluster overlap that is present in the initial clustering mixture. The result is a particularly user-friendly drawing of the clusters

    更新日期:2020-07-13
  • Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning
    J. Classif. (IF 1.156) Pub Date : 2020-07-06
    Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, Roman Hornung

    In many application areas, prediction rules trained based on high-dimensional data are subsequently applied to make predictions for observations from other sources, but they do not always perform well in this setting. This is because data sets from different sources can feature (slightly) differing distributions, even if they come from similar populations. In the context of high-dimensional data and

    更新日期:2020-07-06
  • Model-based Clustering of Count Processes
    J. Classif. (IF 1.156) Pub Date : 2020-07-02
    Tin Lok James Ng, Thomas Brendan Murphy

    A model-based clustering method based on Gaussian Cox process is proposed to address the problem of clustering of count process data. The model allows for nonparametric estimation of intensity functions of Poisson processes, while simultaneous clustering count process observations. A logistic Gaussian process transformation is imposed on the intensity functions to enforce smoothness. Maximum likelihood

    更新日期:2020-07-02
  • Adjusted Concordance Index: an Extensionl of the Adjusted Rand Index to Fuzzy Partitions
    J. Classif. (IF 1.156) Pub Date : 2020-06-15
    Antonio D’Ambrosio, Sonia Amodio, Carmela Iorio, Giuseppe Pandolfo, Roberta Siciliano

    In comparing clustering partitions, the Rand index (RI) and the adjusted Rand index (ARI) are commonly used for measuring the agreement between partitions. Such external validation indexes can be used to quantify how close the clusters are to a reference partition (or to prior knowledge about the data) by counting classified pairs of elements. To evaluate the solution of a fuzzy clustering algorithm

    更新日期:2020-06-18
  • “Compositional Data Analysis in Practice” by Michael Greenacre Universitat Pompeu Fabra (Barcelona, Spain), Chapman and Hall/CRC, 2018
    J. Classif. (IF 1.156) Pub Date : 2020-05-18
    J. A. Martín-Fernández

    This 122-page book is intended to be a practical guide to CoDa analysis and its easyto-read format and didactic layout are designed for students and researchers alike from different fields. For more insight, the interested reader can find other books that present the subject in a more up-to-date manner and cover more multivariate techniques with applications and examples from geochemistry.

    更新日期:2020-05-18
  • A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting
    J. Classif. (IF 1.156) Pub Date : 2020-03-04
    Sanjeena Subedi, Paul D. McNicholas

    Mixture model-based clustering has become an increasingly popular data analysis technique since its introduction over fifty years ago, and is now commonly utilized within a family setting. Families of mixture models arise when the component parameters, usually the component covariance (or scale) matrices, are decomposed and a number of constraints are imposed. Within the family setting, model selection

    更新日期:2020-03-04
  • Consumer Segmentation Based on Use Patterns
    J. Classif. (IF 1.156) Pub Date : 2020-02-19
    Juan José Fernández-Durán, María Mercedes Gregorio-Domínguez

    Recent technological advances have enabled the easy collection of consumer behavior data in real time. Typically, these data contain the time at which a consumer engages in a particular activity such as entering a store, buying a product, or making a call. The occurrence time of certain events must be analyzed as circular random variables, with 24:00 corresponding to 0:00. To effectively implement

    更新日期:2020-02-19
  • Spherical Classification of Data, a New Rule-Based Learning Method
    J. Classif. (IF 1.156) Pub Date : 2020-02-18
    Zhengyu Ma, Hong Seo Ryoo

    This paper presents a new rule-based classification method that partitions data under analysis into spherical patterns. The forte of the method is twofold. One, it exploits the efficiency of distance metric-based clustering to fast collect similar data into spherical patterns. The other, spherical patterns are each a trait shared among one type of data only, hence are built for classification of new

    更新日期:2020-02-18
  • Modified Subspace Constrained Mean Shift Algorithm
    J. Classif. (IF 1.156) Pub Date : 2020-02-11
    Youness Aliyari Ghassabeh, Frank Rudzicz

    A subspace constrained mean shift (SCMS) algorithm is a non-parametric iterative technique to estimate principal curves. Principal curves, as a nonlinear generalization of principal components analysis (PCA), are smooth curves (or surfaces) that pass through the middle of a data set and provide a compact low-dimensional representation of data. The SCMS algorithm combines the mean shift (MS) algorithm

    更新日期:2020-02-11
  • A New Performance Evaluation Metric for Classifiers: Polygon Area Metric
    J. Classif. (IF 1.156) Pub Date : 2020-01-25
    Onder Aydemir

    Classifier performance assessment (CPA) is a challenging task for pattern recognition. In recent years, various CPA metrics have been developed to help assess the performance of classifiers. Although the classification accuracy (CA), which is the most popular metric in pattern recognition area, works well if the classes have equal number of samples, it fails to evaluate the recognition performance

    更新日期:2020-01-25
  • A Membership Probability–Based Undersampling Algorithm for Imbalanced Data
    J. Classif. (IF 1.156) Pub Date : 2020-01-14
    Gilseung Ahn, You-Jin Park, Sun Hur

    Classifiers for a highly imbalanced dataset tend to bias in majority classes and, as a result, the minority class samples are usually misclassified as majority class. To overcome this, a proper undersampling technique that removes some majority samples can be an alternative. We propose an efficient and simple undersampling method for imbalanced datasets and show that the proposed method outperforms

    更新日期:2020-01-14
  • A Note on the Formal Implementation of the K -means Algorithm with Hard Positive and Negative Constraints
    J. Classif. (IF 1.156) Pub Date : 2020-01-10
    Igor Melnykov, Volodymyr Melnykov

    The paper discusses a new approach for incorporating hard constraints into the K-means algorithm for semi-supervised clustering. An analytic modification of the objective function of K-means is proposed that has not been previously considered in the literature.

    更新日期:2020-01-10
  • An Impartial Trimming Approach for Joint Dimension and Sample Reduction
    J. Classif. (IF 1.156) Pub Date : 2020-01-09
    Luca Greco, Antonio Lucadamo, Pietro Amenta

    A robust version of reduced and factorial k-means is proposed that is based on the idea of trimming. Reduced and factorial k-means are data reduction techniques well suited for simultaneous dimension and sample reduction through PCA and clustering. The occurrence of data inadequacies can invalidate standard analyses. Actually, contamination in the data at hand can hide the underlying clustered structure

    更新日期:2020-01-09
  • Lorenz Model Selection
    J. Classif. (IF 1.156) Pub Date : 2020-01-08
    Paolo Giudici, Emanuela Raffinetti

    In the paper, we introduce novel model selection measures based on Lorenz zonoids which, differently from measures based on correlations, are based on a mutual notion of variability and are more robust to the presence of outlying observations. By means of Lorenz zonoids, which in the univariate case correspond to the Gini coefficient, the contribution of each explanatory variable to the predictive

    更新日期:2020-01-08
  • C443: a Methodology to See a Forest for the Trees
    J. Classif. (IF 1.156) Pub Date : 2020-01-07
    Aniek Sies, Iven Van Mechelen

    Often tree-based accounts of statistical learning problems yield multiple decision trees which together constitute a forest. Reasons for this include examining tree instability, improving prediction accuracy, accounting for missingness in the data, and taking into account multiple outcome variables. A key disadvantage of forests, unlike individual decision trees, is their lack of transparency. Hence

    更新日期:2020-01-07
  • Cognitive Diagnostic Computerized Adaptive Testing for Polytomously Scored Items
    J. Classif. (IF 1.156) Pub Date : 2019-12-24
    Xuliang Gao, Daxun Wang, Yan Cai, Dongbo Tu

    Cognitive diagnostic computerized adaptive testing (CD-CAT) purports to combine the strengths of both CAT and cognitive diagnosis. Currently, large number of CD-CAT researches focus on the dichotomous data. In our knowledge, there are no researches on CD-CAT for polytomously scored items or data. However, polytomously scored items have been broadly used in a variety of tests for their advantages of

    更新日期:2019-12-24
  • ROC and AUC with a Binary Predictor: a Potentially Misleading Metric
    J. Classif. (IF 1.156) Pub Date : 2019-12-23
    John Muschelli

    In analysis of binary outcomes, the receiver operator characteristic (ROC) curve is heavily used to show the performance of a model or algorithm. The ROC curve is informative about the performance over a series of thresholds and can be summarized by the area under the curve (AUC), a single number. When a predictor is categorical, the ROC curve has one less than number of categories as potential thresholds;

    更新日期:2019-12-23
  • Proximity Curves for Potential-Based Clustering
    J. Classif. (IF 1.156) Pub Date : 2019-12-18
    Attila Csenki, Daniel Neagu, Denis Torgunov, Natasha Micic

    The concept of proximity curve and a new algorithm are proposed for obtaining clusters in a finite set of data points in the finite dimensional Euclidean space. Each point is endowed with a potential constructed by means of a multi-dimensional Cauchy density, contributing to an overall anisotropic potential function. Guided by the steepest descent algorithm, the data points are successively visited

    更新日期:2019-12-18
  • An Optimal Weight Semi-Supervised Learning Machine for Neural Networks with Time Delay
    J. Classif. (IF 1.156) Pub Date : 2019-12-10
    Chengbo Lu, Ying Mei

    In this paper, an optimal weight semi-supervised learning machine for a single-hidden layer feedforward network (SLFN) with time delay is developed. Both input weights and output weights of the SLFN are globally optimized with manifold regularization. By feature mapping, input vectors can be placed at the prescribed positions in the feature space in the sense that the separability of all nonlinearly

    更新日期:2019-12-10
  • Two-Stage Metropolis-Hastings for Tall Data.
    J. Classif. (IF 1.156) Pub Date : 2018-10-06
    Richard D Payne,Bani K Mallick

    This paper discusses the challenges presented by tall data problems associated with Bayesian classification (specifically binary classification) and the existing methods to handle them. Current methods include parallelizing the likelihood, subsampling, and consensus Monte Carlo. A new method based on the two-stage Metropolis-Hastings algorithm is also proposed. The purpose of this algorithm is to reduce

    更新日期:2019-11-01
  • Outlier Identification in Model-Based Cluster Analysis.
    J. Classif. (IF 1.156) Pub Date : 2016-01-26
    Katie Evans,Tanzy Love,Sally W Thurston

    In model-based clustering based on normal-mixture models, a few outlying observations can influence the cluster structure and number. This paper develops a method to identify these, however it does not attempt to identify clusters amidst a large field of noisy observations. We identify outliers as those observations in a cluster with minimal membership proportion or for which the cluster-specific variance

    更新日期:2019-11-01
  • Are We Underestimating Food Insecurity? Partial Identification with a Bayesian 4-Parameter IRT Model
    J. Classif. (IF 1.156) Pub Date : 2019-10-02
    Christian A. Gregory

    This paper addresses measurement error in food security in the USA. In particular, it uses a Bayesian 4-parameter IRT model to look at the likelihood of over- or under-reporting of the conditions that comprise the food security module (FSM), the data collection administered in many US surveys to assess and monitor food insecurity. While this model’s parameters are only partially identified, we learn

    更新日期:2019-10-02
  • Erratum to: A Framework for Quantifying Qualitative Responses in Pairwise Experiments
    J. Classif. (IF 1.156) Pub Date : 2019-08-14
    A. H. Al-Ibrahim

    The original version of this article unfortunately contained a mistake in Title and reference Thurstone, L. L. (1927).

    更新日期:2019-08-14
  • Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition
    J. Classif. (IF 1.156) Pub Date : 2019-07-16
    Salvatore Ingrassia, Antonio Punzo

    One of the challenges in cluster analysis is the evaluation of the obtained clustering results without using auxiliary information. To this end, a common approach is to use internal validity criteria. For mixtures of linear regressions whose parameters are estimated by maximum likelihood, we propose a three-term decomposition of the total sum of squares as a starting point to define some internal validity

    更新日期:2019-07-16
  • A Modified k -Means Clustering Procedure for Obtaining a Cardinality-Constrained Centroid Matrix
    J. Classif. (IF 1.156) Pub Date : 2019-07-16
    Naoto Yamashita, Kohei Adachi

    k-means clustering is a well-known procedure for classifying multivariate observations. The resulting centroid matrix of clusters by variables is noted for interpreting which variables characterize clusters. However, between-clusters differences are not always clearly captured in the centroid matrix. We address this problem by proposing a new procedure for obtaining a centroid matrix, so that it has

    更新日期:2019-07-16
  • An Ensemble Feature Ranking Algorithm for Clustering Analysis
    J. Classif. (IF 1.156) Pub Date : 2019-07-11
    Jaehong Yu, Hua Zhong, Seoung Bum Kim

    Feature ranking is a widely used feature selection method. It uses importance scores to evaluate features and selects those with high scores. Conventional unsupervised feature ranking methods do not consider the information on cluster structures; therefore, these methods may be unable to select the relevant features for clustering analysis. To address this limitation, we propose a feature ranking algorithm

    更新日期:2019-07-11
  • Suboptimal Comparison of Partitions
    J. Classif. (IF 1.156) Pub Date : 2019-07-11
    Jonathon J. O’Brien, Michael T. Lawson, Devin K. Schweppe, Bahjat F. Qaqish

    The distinction between classification and clustering is often based on a priori knowledge of classification labels. However, in the purely theoretical situation where a data-generating model is known, the optimal solutions for clustering do not necessarily correspond to optimal solutions for classification. Exploring this divergence leads us to conclude that no standard measures of either internal

    更新日期:2019-07-11
  • Where Should I Submit My Work for Publication? An Asymmetrical Classification Model to Optimize Choice
    J. Classif. (IF 1.156) Pub Date : 2019-07-11
    A. Ferrer-Sapena, J. M. Calabuig, L. M. García Raffi, E. A. Sánchez Pérez

    Choosing a journal to publish a work is a task that involves many variables. Usually, the authors’ experience allows them to classify journals into categories, according to their suitability and the characteristics of the article. However, there are certain aspects in the choice that are probabilistic in nature, whose modelling may provide some help. Suppose an author has to choose a journal from a

    更新日期:2019-07-11
  • A Theoretical Analysis of the Peaking Phenomenon in Classification
    J. Classif. (IF 1.156) Pub Date : 2019-07-11
    Amin Zollanvari, Alex Pappachen James, Reza Sameni

    In this work, we analytically study the peaking phenomenon in the context of linear discriminant analysis in the multivariate Gaussian model under the assumption of a common known covariance matrix. The focus is finite-sample setting where the sample size and observation dimension are comparable. Therefore, in order to study the phenomenon in such a setting, we use an asymptotic technique whereby the

    更新日期:2019-07-11
  • Adjusting Person Fit Index for Skewness in Cognitive Diagnosis Modeling
    J. Classif. (IF 1.156) Pub Date : 2019-07-11
    Kevin Carl P. Santos, Jimmy de la Torre, Matthias von Davier

    Because the validity of diagnostic information generated by cognitive diagnosis models (CDMs) depends on the appropriateness of the estimated attribute profiles, it is imperative to ensure the accurate measurement of students’ test performance by conducting person fit (PF) evaluation to avoid flawed remediation measures. The standardized log-likelihood statistic lZ has been extended to the CDM framework

    更新日期:2019-07-11
  • Erratum to: Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model
    J. Classif. (IF 1.156) Pub Date : 2019-05-31
    Yuan Liu, Hongyun Liu

    The authors missed an important reference “Liu, Luo, & Liu, 2014” on the original version of this article.

    更新日期:2019-05-31
  • Classification for Time Series Data. An Unsupervised Approach Based on Reduction of Dimensionality
    J. Classif. (IF 1.156) Pub Date : 2019-05-11
    M. Isabel Landaluce-Calvo, Juan I. Modroño-Herrán

    In this work we use a novel methodology for the classification of time series data, through a natural, unsupervised data learning process. This strategy is based on the sequential use of Multiple Factor Analysis and an ascending Hierarchical Classification Analysis. These two exploratory techniques complement each other and allow for a clustering of the series based on their time paths and on the reduction

    更新日期:2019-05-11
  • Mixtures of Hidden Truncation Hyperbolic Factor Analyzers
    J. Classif. (IF 1.156) Pub Date : 2019-05-02
    Paula M. Murray, Ryan P. Browne, Paul D. McNicholas

    The mixture of factor analyzers model was first introduced over 20 years ago and, in the meantime, has been extended to several non-Gaussian analogs. In general, these analogs account for situations with heavy tailed and/or skewed clusters. An approach is introduced that unifies many of these approaches into one very general model: the mixture of hidden truncation hyperbolic factor analyzers (MHTHFA)

    更新日期:2019-05-02
  • Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model
    J. Classif. (IF 1.156) Pub Date : 2019-04-30
    Yuan Liu, Hongyun Liu

    The piecewise growth mixture model is used in longitudinal studies to tackle non-continuous trajectories and unobserved heterogeneity in a compound way. This study investigated how factors such as latent distance and shape influence the model. Two simulation studies were used exploring the 2- and 3-class situation with sample size, latent distance (Mahalanobis distance), and shape being considered

    更新日期:2019-04-30
  • Improving a Centroid-Based Clustering by Using Suitable Centroids from Another Clustering
    J. Classif. (IF 1.156) Pub Date : 2019-04-24
    Mohammad Rezaei

    Fast centroid-based clustering algorithms such as k-means usually converge to a local optimum. In this work, we propose a method for constructing a better clustering from two such suboptimal clustering solutions based on the fact that each suboptimal clustering has benefits regarding to including some of the correct clusters. We develop the new method COTCLUS to find two centroids from one clustering

    更新日期:2019-04-24
  • A Partial Mastery, Higher-Order Latent Structural Model for Polytomous Attributes in Cognitive Diagnostic Assessments
    J. Classif. (IF 1.156) Pub Date : 2019-04-22
    Peida Zhan, Wen-Chung Wang, Xiaomin Li

    The latent attribute space in cognitive diagnosis models (CDMs) is often assumed to be unstructured or saturated. In recent years, the number of latent attributes in real tests has often been found to be large, and polytomous latent attributes have been advocated. Therefore, it is preferable to adopt substantive theories to connect seemingly unrelated latent attributes, to replace the unstructured

    更新日期:2019-04-22
  • Bayesian Local Influence of Generalized Failure Time Models with Latent Variables and Multivariate Censored Data
    J. Classif. (IF 1.156) Pub Date : 2019-04-05
    Ming Ouyang, Xinyuan Song

    We develop a Bayesian local influence procedure for generalized failure time models with latent variables and multivariate censored data. We propose to use the penalized splines (P-splines) approach to formulate the unknown functions of the proposed models. We assess the effects of minor perturbations to individual observations, the prior distributions of parameters, and the sampling distribution on

    更新日期:2019-04-05
  • Moduli Space of Families of Positive ( n − 1)-Weights
    J. Classif. (IF 1.156) Pub Date : 2019-04-05
    Simone Calamai

    We show the geometrical structure of the moduli space of positive-weighted trees with n labels 1,…,n which realize the same family of positive (n − 1)-weights and we characterize them as a family of positive multi-weights.

    更新日期:2019-04-05
  • Clique-Based Method for Social Network Clustering
    J. Classif. (IF 1.156) Pub Date : 2019-04-02
    Guang Ouyang, Dipak K. Dey, Panpan Zhang

    In this article, we develop a clique-based method for social network clustering. We introduce a new index to evaluate the quality of clustering results, and propose an efficient algorithm based on recursive bipartition to maximize an objective function of the proposed index. The optimization problem is NP-hard, so we approximate the semi-optimal solution via an implicitly restarted Lanczos method.

    更新日期:2019-04-02
  • A Nonparametric Estimator of Bivariate Quantile Residual Life Model with Application to Tumor Recurrence Data Set
    J. Classif. (IF 1.156) Pub Date : 2019-04-02
    M. Kayid, M. Shafaei Noughabi, A. M. Abouammoh

    Recently, Shafaei and Kayid (Statistical Papers, 2017) introduced and studied the bivariate quantile residual life model. It has been shown that two suitable bivariate quantile residual life functions characterize the underlying distribution uniquely. In the current investigation, we first propose a nonparametric estimator of this new model. The estimator is strongly consistent and, on proper normalization

    更新日期:2019-04-02
  • Accurate Bayesian Data Classification Without Hyperparameter Cross-Validation
    J. Classif. (IF 1.156) Pub Date : 2019-04-02
    Mansoor Sheikh, A. C. C. Coolen

    We extend the standard Bayesian multivariate Gaussian generative data classifier by considering a generalization of the conjugate, normal-Wishart prior distribution, and by deriving the hyperparameters analytically via evidence maximization. The behaviour of the optimal hyperparameters is explored in the high-dimensional data regime. The classification accuracy of the resulting generalized model is

    更新日期:2019-04-02
  • Totally Balanced Dissimilarities
    J. Classif. (IF 1.156) Pub Date : 2019-03-30
    François Brucker, Pascal Préa, Célia Châtel

    We show in this paper a bijection between totally balanced hypergraphs and so-called totally balanced dissimilarities. We give an efficient way (O(n3) where n is the number of elements) to (i) recognize if a given dissimilarity is totally balanced and (ii) approximate it if it is not the case. We also introduce a new kind of dissimilarity which generalizes chordal graphs and allows a polynomial number

    更新日期:2019-03-30
  • Variable Selection for Mixed Data Clustering: Application in Human Population Genomics
    J. Classif. (IF 1.156) Pub Date : 2019-03-30
    Matthieu Marbac, Mohammed Sedki, Tienne Patin

    Model-based clustering of human population genomic data, composed of 1,318 individuals arisen from western Central Africa and 160,470 markers, is considered. This challenging analysis leads us to develop a new methodology for variable selection in clustering. To explain the differences between subpopulations and to increase the accuracy of the estimates, variable selection is done simultaneously to

    更新日期:2019-03-30
  • Clustering Analysis of a Dissimilarity: a Review of Algebraic and Geometric Representation
    J. Classif. (IF 1.156) Pub Date : 2019-03-30
    D. Fortin

    It is customary to split clustering analysis into an optimization level, then a (preferably) graphical representation level to take benefit of human vision for an effective understanding of (big) data structure. This article aspires to clarify relationships between clustering, both its process and its representation, and the underlying structural graph properties, both algebraic and geometric, starting

    更新日期:2019-03-30
  • On Cesáro Averages for Weighted Trees in the Random Forest
    J. Classif. (IF 1.156) Pub Date : 2019-03-30
    Hieu Pham, Sigurður Olafsson

    The random forest is a popular and effective classification method. It uses a combination of bootstrap resampling and subspace sampling to construct an ensemble of decision trees that are then averaged for a final prediction. In this paper, we propose a potential improvement on the random forest that can be thought of as applying a weight to each tree before averaging. The new method is motivated by

    更新日期:2019-03-30
  • An Algorithm for Ordinal Classification Based on Pairwise Comparison
    J. Classif. (IF 1.156) Pub Date : 2019-03-30
    Yunli Yang, Baiyu Chen, Zhouwang Yang

    Ordinal classification problems are applied in many fields. In the field of multivariate statistical analysis, these tasks are referred to as ordinal regression problems. In the field of management decision-making, they are known as multi-criteria decision analyses or sorting problems. This paper introduces the PairCode algorithm for ordinal classification with small sample sizes, which is based on

    更新日期:2019-03-30
  • Core Clustering as a Tool for Tackling Noise in Cluster Labels
    J. Classif. (IF 1.156) Pub Date : 2019-03-30
    Renato Cordeiro de Amorim, Vladimir Makarenkov, Boris Mirkin

    Real-world data sets often contain mislabelled entities. This can be particularly problematic if the data set is being used by a supervised classification algorithm at its learning phase. In this case, the accuracy of this classification algorithm, when applied to unlabelled data, is likely to suffer considerably. In this paper, we introduce a clustering-based method capable of reducing the number

    更新日期:2019-03-30
  • Clustering Large Datasets by Merging K -Means Solutions
    J. Classif. (IF 1.156) Pub Date : 2019-03-29
    Volodymyr Melnykov, Semhar Michael

    Existing clustering methods range from simple but very restrictive to complex but more flexible. The K-means algorithm is one of the most popular clustering procedures due to its computational speed and intuitive construction. Unfortunately, the application of K-means in its traditional form based on Euclidean distances is limited to cases with spherical clusters of approximately the same volume and

    更新日期:2019-03-29
  • Binary Whale Optimization Algorithm and Binary Moth Flame Optimization with Clustering Algorithms for Clinical Breast Cancer Diagnoses
    J. Classif. (IF 1.156) Pub Date : 2019-03-29
    Gehad Ismail Sayed, Ashraf Darwish, Aboul Ella Hassanien

    Models based on machine learning algorithms have been developed to detect the breast cancer disease early. Feature selection is commonly applied to improve the performance of these models through selecting only relevant features. However, selecting relevant features in unsupervised learning is much difficult. This is due to the absence of class labels that guide the search for relevant information

    更新日期:2019-03-29
  • Asking Infinite Voters ‘Who is a J?’: Group Identification Problems in ℕ$\mathbb {N}$
    J. Classif. (IF 1.156) Pub Date : 2019-03-29
    Federico Fioravanti, Fernando Tohmé

    We analyze the problem of classifying individuals in a group N taking into account their opinions about which of them should belong to a specific subgroup N′⊆ N, in the case that |N| > ∞. We show that this problem is relevant in cases in which the group changes in time and/or is subject to uncertainty. The approach followed here to find the ensuing classification is by means of a Collective Identity

    更新日期:2019-03-29
  • Pointed Subspace Approach to Incomplete Data
    J. Classif. (IF 1.156) Pub Date : 2019-03-28
    Lukasz Struski, Marek Śmieja, Jacek Tabor

    Incomplete data are often represented as vectors with filled missing attributes joined with flag vectors indicating missing components. In this paper, we generalize this approach and represent incomplete data as pointed affine subspaces. This allows to perform various affine transformations of data, such as whitening or dimensionality reduction. Moreover, this representation preserves the information

    更新日期:2019-03-28
  • A Hybrid Fuzzy Maintained Classification Method Based on Dendritic Cells
    J. Classif. (IF 1.156) Pub Date : 2019-03-28
    Zaineb Chelly Dagdia, Zied Elouedi

    The dendritic cell algorithm (DCA) is a classification algorithm based on the behavior of natural dendritic cells (DCs). In literature, DCA has given good classification results. However, DCA was known to be sensitive to the order of the instance classes. To solve this limitation, a fuzzy DCA version was developed stating that the cause of such sensitivity is related to the DCA crisp classification

    更新日期:2019-03-28
  • Ordinal Forests
    J. Classif. (IF 1.156) Pub Date : 2019-01-22
    Roman Hornung

    The ordinal forest method is a random forest–based prediction method for ordinal response variables. Ordinal forests allow prediction using both low-dimensional and high-dimensional covariate data and can additionally be used to rank covariates with respect to their importance for prediction. An extensive comparison study reveals that ordinal forests tend to outperform competitors in terms of prediction

    更新日期:2019-01-22
  • Quantum-Behaved Particle Swarm Optimization for Parameter Optimization of Support Vector Machine
    J. Classif. (IF 1.156) Pub Date : 2019-01-21
    Alaa Tharwat, Aboul Ella Hassanien

    Support vector machine (SVM) parameters such as penalty parameter and kernel parameters have a great influence on the complexity and accuracy of SVM model. In this paper, quantum-behaved particle swarm optimization (QPSO) has been employed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (QPSO-SVM), the experiment adopted seven standard

    更新日期:2019-01-21
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