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Combining clustering of variables and feature selection using random forests

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2021年第2期50卷 426-445页

作者： Chavent, Marie Genuer, Robin Saracco, Jerome Univ Bordeaux CNRS UMR 5251 Inst Math Bordeaux Talence France INRIA Bordeaux Sud Ouest CQFD Team Talence France Univ Bordeaux INSERM U1219 ISPED 146 Rue Leo Saignat F-33076 Bordeaux France INRIA Bordeaux Sud Ouest SISTM Team Talence France

Standard approaches to tackle high-dimensional supervised classification often include variable selection and dimension reduction. The proposed methodology combines clustering of variables and feature selection. Hierarchical clustering of variables allows to built groups of correlated variables and summarizes each group by a synthetic variable. Originality is that groups of variables are unknown a priori. Moreover clustering approach deals with both numerical and categorical variables. Among all the possible partitions, the most relevant synthetic variables are selected with a procedure using random forests. Numerical performances are illustrated on simulated and real datasets. Selection of groups of variables provides easier interpretation of results.

关键词： clustering of variables random forests supervised classification variable selection

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Combining Multiple Feature-Ranking Techniques and clustering of variables for Feature Selection

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IEEE ACCESS 2019年 7卷 151482-151492页

作者： Ul Haq, Anwar Zhang, Defu Peng, He Rahman, Sami Ur Xiamen Univ Sch Informat Sci & Engn Xiamen 361005 Fujian Peoples R China Univ Malakand Dept Comp Sci Malakand Pakistan

Feature selection aims to eliminate redundant or irrelevant variables from input data to reduce computational cost, provide a better understanding of data and improve prediction accuracy. Majority of the existing filter methods utilize a single feature-ranking technique, which may overlook some important assumptions about the underlying regression function linking input variables with the output. In this paper, we propose a novel feature selection framework that combines clustering of variables with multiple feature-ranking techniques for selecting an optimal feature subset. Different feature-ranking methods typically result in selecting different subsets, as each method has its own assumption about the regression function linking input variables with the output. Therefore, we employ multiple feature-ranking methods having disjoint assumption about the regression function. The proposed approach has a feature ranking module to identify relevant features and a clustering module to eliminate redundant features. First, input variables are ranked using regression coefficients obtained by training $L1$ regularized Logistic Regression, Support Vector Machine and Random Forests models. Those features which are ranked lower than a certain threshold are filtered-out. The remaining features are grouped into clusters using an exemplar-based clustering algorithm, which identifies data-points that exemplify the data better, and associates each data-point with an exemplar. We use both linear correlation coefficients and information gain for measuring the association between a data-point and its corresponding exemplar. From each cluster the highest ranked feature is selected as a delegate, and all delegates from the three ranked lists are combined into the final feature set using union operation. Empirical results over a number of real-world data sets confirm the hypothesis that combining features selected using multiple heterogeneous methods results in a more robust feature set an

关键词： Feature extraction Correlation Input variables Prediction algorithms Training Support vector machines Diversity reception Classification clustering of variables feature selection filter methods random forests

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clustering of variables in regression analysis: a comparative study between different algorithms

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JOURNAL OF CHEMOMETRICS 2013年第10期27卷 306-317页

作者： Hemmateenejad, Bahram Karimi, Sadegh Mobaraki, Nabiollah Shiraz Univ Dept Chem Shiraz Iran

We have recently suggested using clustering of variables (CLoVA) based on unsupervised pattern recognition for partitioning variables into informative and redundant ones. Because data clustering plays a central role in CLoVA, in the present study, we compared the efficiency of different clustering methods including the Kohonen self-organizing map (SOM), principal component analysis, fuzzy c-means clustering, K-means clustering, and hierarchical cluster analysis for clustering spectroscopic data and molecular descriptors to build multivariate calibration and quantitative structure-activity (QSAR) models. To investigate which clustering methods are more efficient for CLoVA, four data sets (three spectroscopic and one QSAR) were analyzed. Most of the CLoVA-based models obtained by SOM resulted in the least root mean square errors of cross-validation and prediction, suggesting a higher efficiency of SOM for clustering variables. In all cases, the results obtained by the CLoVA-based method were compared with those obtained by conventional principal component regression as well as genetic algorithm and successive projection algorithm partial least square regression. Interestingly, models produced by the CLoVA-based method were more predictive with respect to that of the other methods, as indicated by the lowest root mean square error of prediction. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： clustering of variables multivariate calibration QSAR clustering algorithm self-organizing map

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Princilpal cluster component analysis revisited: A tool for scale development

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Behaviormetrika 2025年 1-26页

作者： Murakami, Takashi Institute of Cultural Science Chukyo University 2-8-8-804 Deki-machi Higashi-ku Nagoya 461-0032 Japan

To construct a scale consisting of multiple sum scores based on item responses, we formulated a constrained Principal Component Analysis (PCA) that produces exclusive and exhaustive clusters of items. By replacing the loadings of each item with a single regression coefficient and zeros, the loading matrix is transformed into a perfect simple structure. This method reduces to a non-hierarchical clustering of variables, where the sum of the largest eigenvalues of correlation matrices for clusters is maximized, which is equivalent to the Principal Cluster Component Analysis (PCCA) developed in the 1970 s but not widely known. This straightforward method yields solutions comparable to more recently developed, sophisticated methods. PCCA performed better for defining sum scores with maximum coefficient α than the common procedure using Exploratory Factor Analysis (EFA) when analyzing a real data set. © The Behaviormetric Society 2024.

关键词： Bootstrap method clustering of variables Internal consistency Principal components Reliability Sum scores

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Identification of discriminatory variables in proteomics data analysis by clustering of variables

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ANALYTICA CHIMICA ACTA 2013年第1期767卷 35-43页

作者： Karimi, Sadegh Hemmateenejad, Bahram Shiraz Univ Med Sci Med & Nat Prod Chem Res Ctr Shiraz Iran Shiraz Univ Dept Chem Shiraz Iran

This article presents a data analysis method for biomarker discovery in proteomics data analysis. In factor analysis-based discriminate models, the latent variables (LV's) are calculated from the response data measured at all employed instrument channels. Since some channels are irrelevant and their responses do not possess useful information, the extracted LV's possess mixed information from both useful and irrelevant channels. In this work, clustering of variables (CLoVA) based on unsupervised pattern recognition is suggested as an efficient method to identify the most informative spectral region and then it is used to construct a more predictive multivariate classification model. In the suggested method, the instrument channels (m/z value) are clustered into different clusters via self-organization map. Subsequently, the spectral data of each cluster are separately used as the input variables of classification methods such as partial least square-discriminate analysis (PLS-DA) and extended canonical variate analysis (ECVA). The proposed method is evaluated by the analysis of two experimental data sets (ovarian and prostate cancer data set). It is found that our proposed method is able to detect cancerous from healthy samples with much higher sensitivity and selectivity than conventional PLS-DA and ECVA methods. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Classification Proteomics clustering of variables Cancer Discriminant analysis Self-organization map

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Agent Mining Framework for Analyzing Moroccan Olive Oil Datasets

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第12期11卷 638-646页

作者： Imane, Belabed El Miloud, Jaara Abdelmajid, Belabed Mohammed, Talibi Alaoui Univ Mohammed First Oujda Morocco Fac Sci & Technol Fes Morocco

Data mining and intelligent agents have become two promising research areas. Each intelligent agent functions independently while cooperating with other agents, to perform effectively assigned tasks. The main goal of this research, is to provide a mining implementation that can help biological researchers for discovering parameters that affect the cost of olive oil in Morocco. To solve this problem, we used a method involving two data mining techniques, clustering of variables, quantitative association rules and multi-agent system to fuse these two techniques. Therefore, we have developed a multi-agent framework that has been validated by using concrete data from the Provincial Direction of Agriculture of Berkane, Morocco. To prove the performance of our framework, we tested the proposed multi-agent tool using three datasets from different fields. Conforming to biological researchers, our method generates a clear knowledge because the framework proposes high-confidence rules that can correctly identify olive oil factors.

关键词： Quantitative association rules clustering of variables multi-agent system

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Certain relationships between Animal Performance, Sensory Quality and Nutritional Quality can be generalized between various experiments on animal of similar types

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LIVESTOCK SCIENCE 2021年 250卷 104554-104554页

作者： Ellies-Oury, M-P Durand, D. Listrat, A. Chavent, M. Saracco, J. Gruffat, D. Bordeaux Sci Agro 1 Cours Gen GaulleCS 40201 F-40201 Gradignan France INRAE UMR1213 Herbivores F-63122 St Genes Champanelle France Clermont Univ UMR1213 VetAgro Sup Herbivores BP 10448 F-63000 Clermont Ferrand France Univ Bordeaux INRIA UMR5251 F-33400 Talence France

In the beef sector, one of the major challenges is to early predict carcass and meat quality and to jointly satisfy the multiple expectations of the various stakeholders. Thus, the objective of this study was to determine if the relationships among carcass, nutritional and sensory qualities established previously by Ellies-Oury et al. (2016) might be generalized to different type of animals. The Longissimus thoracis muscles of 32 young Charolais bulls were analyzed in terms of sensory and nutritional quality (lipid content and fatty acid composition). These parameters of interest were linked together and to animal performances by using a clustering of variables. Longissimus thoracis sensory and nutritional qualities appear sometimes antagonistic. Indeed, some "positive" sensory descriptors (juiciness, overall appreciation, overall flavor and overall odor) are negatively related to PUFA proportions. PUFA proportions are positively associated with carcass weight but in the same time with rancid/fish flavors. Moreover, CLA and trans MUFA proportions are positively associated with the "negative" descriptors of greasy feel and residues. To finish, carcass weight and ADG are negatively associated with some "positive" sensory descriptors except tenderness. It can be concluded from this work that these relationships, that were already established in previous works, are robust between experiments. In order to highlight robust and generalizable relationships in different contexts, it is now appropriate to apply this method to a larger database containing different traits and various characteristics of breed, slaughter ages, animal types, fattening practices, ...

关键词： clustering of variables Animal performances Meat quality traits Fatty acid composition Nutritional value Prediction

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Consumer segmentation in multi-attribute product evaluation by means of non-negatively constrained CLV3W

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FOOD QUALITY AND PREFERENCE 2018年 67卷 18-26页

作者： Cariou, Veronique Wilderjans, Tom F. INRA StatSC ONIRIS F-44322 Nantes France Leiden Univ Fac Social & Behav Sci Inst Psychol Methodol & Stat Res Unit Pieter Court BldgWassenaarseweg 52 NL-2333 AK Leiden Netherlands Katholieke Univ Leuven Fac Psychol & Educ Sci Res Grp Quantitat Psychol & Individual Difference Tiensestr 102Box 3713 B-3000 Leuven Belgium

In consumer studies, segmentation has been widely applied to identify consumer subsets on the basis of their preference for a set of products. From the last decade onwards, a more comprehensive evaluation of product performance has led to take into account various information such as consumer emotion assessment or hedonic measures on several aspects, like taste, visual and flavor. This multi-attribute evaluation of products naturally yields a three-way (products by consumers by attributes) data structure. In order to identify segments of consumers on the basis of such three-way data, the Three-Way Cluster analysis around Latent variables (CLV3W) approach (Wilderjans & Cariou, 2016) is considered. This method groups the consumers into clusters and estimates for each cluster an associated latent product variable and attribute weights, along with a set of consumer loadings, which may be used for the purpose of cluster-specific product characterization. As consumers who rate the products along the attributes in an opposite way (i.e., raters' disagreement) should not be in the same cluster, in this paper, we propose to add a non-negativity constraint on the consumer loadings and to integrate this constraint within the versatile CLV3W approach. This non-negatively constrained criterion implies that the latent variable for each cluster is determined such that consumers within each cluster are as much related - in terms of a positive covariance - as possible with this latent product component. This approach is applied to a consumer emotion ratings dataset related to coffee aromas. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Consumer segmentation Three-way structure clustering of variables CLV CLV3W Clustetwise Parafac Latent variables Acceptance patterns Non-negativity

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CLV3W: A clustering around latent variables approach to detect panel disagreement in three-way conventional sensory profiling data

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FOOD QUALITY AND PREFERENCE 2016年第PartA期47卷 45-53页

作者： Wilderjans, Tom F. Cariou, Veronique Katholieke Univ Leuven Fac Psychol & Educ Sci Methodol Educ Sci Res Grp B-3000 Louvain Belgium Leiden Univ Fac Social & Behav Sci Inst Psychol Res Grp Methodol & Stat NL-2333 AK Leiden Netherlands LUNAM Univ Unite Sensometrie & Chimiometrie ONIRIS F-44322 Nantes France INRA F-44316 Nantes France

To detect panel disagreement, we propose the clustering around latent variables for three-way data (CLV3W) approach which extends the clustering of variables around latent components (CLV) approach to three-way data typically obtained from a conventional sensory profiling procedure (i.e., assessors rating products on various descriptors). The CLV3W method groups the descriptors into Q clusters and estimates for each cluster an associated latent sensory component such that the attributes within each cluster are as much related (i.e., highest squared covariance) as possible with the latent component. Simultaneously, for each latent sensory component separately, a system of weights is estimated that yields information regarding the extent to which an assessor (dis)agrees with the rest of the panel according to the latent sensory component under study. Our new approach is illustrated with a dataset pertaining to Quantitative Descriptive Analysis applied to cider varieties. It is shown that CLV3W, as opposed to related approaches, is able to detect differential panel disagreement on various latent sensory components. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Sensory profiling Fixed vocabulary Panel (dis)agreement Assessor weight assignment clustering of variables CLV3W Clusterwise Parafac Latent components

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Analysis of Soil Data from Eastern of Morocco Based on Data Mining Process 5th

Analysis of Soil Data from Eastern of Morocco Based on Data ...

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5th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Belabed, Imane Alaoui, Mohammed Talibi Belabed, Abdelmajid Alaoui, Youssef Talibi Mohamed I Univ Fac Sci Oujda Morocco

ISBN: (纸本)9783319561547;9783319561530

Agronomic and agricultural research in East Morocco depends on time and space evolution of the soil, which is an important factor, otherwise, limiting plant cover. Thus, a chemical and physicochemical soil study was assisted by advanced techniques such as data mining. Today, data exploitation on a global scale is used in a large number of vast agribusiness operating areas. Products of computer operating systems and specific domain data extrapolation are applicable across the disciplines;however they are still relatively new on agriculture. This work aims to evaluate the feasibility of the conversion of wheat crops into olive crops, based on the evaluation of the soil's ability of the region to withstand the conversion, which is an axis of the Green Morocco Plan for the horizon of 2020. This paper presents our approach to soil data analysis in order to determine the usefulness of the execution of this strategy, in the eastern of Morocco and its corresponding results.

关键词： Data mining clustering of variables Wheat-olive reconversion Association rules

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