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Bayesian Chain Classifier with Feature Selection for Multi-label Classification

  • Ricardo Benítez JiménezEmail author
  • Eduardo F. Morales
  • Hugo Jair Escalante
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)

Abstract

Multi-label classification task has many applications in Text Categorization, Multimedia, Biology, Chemical data analysis and Social Network Mining, among others. Different approaches have been developed: Binary Relevance (BR), Label Power Set (LPS), Random k label sets (RAkEL), some of them consider the interaction between labels in a chain (Chain Classifier) and other alternatives around this method are derived, for instance, Probabilistic Chain Classifier, Monte Carlo Chain Classifier and Bayesian Chain Classifier (BCC). All previous approaches have in common and focus on is in considering different orders or combinations of the way the labels have to be predicted. Given that feature selection has proved to be important in classification tasks, reducing the dimensionality of the problem and even improving classification model’s accuracy. In this work a feature selection technique is tested in BCC algorithm with two searching methods, one using Best First (BF-FS-BCC) and another with GreedyStepwise (GS-FS-BCC), these methods are compared, the winner is also compared with BCC, both tests are compared through Wilcoxon Signed Rank test, in addition it is compared with others Chain Classifier and finally it is compared with others approaches (BR, RAkEL, LPS).

Keywords

Multi-label classification Chain classifier BCC Feature selection 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ricardo Benítez Jiménez
    • 1
    Email author
  • Eduardo F. Morales
    • 1
  • Hugo Jair Escalante
    • 1
  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)PueblaMexico

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