A simple classification method for class imbalanced data using the kernel mean

Yusuke Sato, Kazuyuki Narisawa, Ayumi Shinohara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Support vector machines (SVMs) are among the most popular classification algorithms. However, whereas SVMs perform efficiently in a class balanced dataset, their performance declines for class imbalanced datasets. The fuzzy SVMfor class imbalance learning (FSVM-CIL) is a variation of the SVMtype algorithm to accommodate class imbalanced datasets. Considering the class imbalance, FSVM-CIL associates a fuzzy membership to each example, which represents the importance of the example for classification. Based on FSVM-CIL, we present a simple but effective method here to calculate fuzzy memberships using the kernel mean. The kernel mean is a useful statistic for consideration of the probability distribution over the feature space. Our proposed method is simpler than preceding methods because it requires adjustment of fewer parameters and operates at reduced computational cost. Experimental results show that our proposed method is promising.

Original languageEnglish
Title of host publicationKDIR 2014 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval
EditorsAna Fred, Joaquim Filipe, Joaquim Filipe
PublisherINSTICC Press
Pages327-334
Number of pages8
ISBN (Electronic)9789897580482
DOIs
Publication statusPublished - 2014
Event6th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2014 - Rome, Italy
Duration: 2014 Oct 212014 Oct 24

Publication series

NameKDIR 2014 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

Other

Other6th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2014
Country/TerritoryItaly
CityRome
Period14/10/2114/10/24

Keywords

  • Class imbalanced learning
  • Fuzzy support vector machine
  • Kernel mean

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications

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