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

Yusuke Sato, Kazuyuki Narisawa, Ayumi Shinohara

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルKDIR 2014 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval
編集者Ana Fred, Joaquim Filipe, Joaquim Filipe
出版社INSTICC Press
ページ327-334
ページ数8
ISBN(電子版)9789897580482
DOI
出版ステータスPublished - 2014
イベント6th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2014 - Rome, Italy
継続期間: 2014 10 212014 10 24

出版物シリーズ

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

Other

Other6th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2014
国/地域Italy
CityRome
Period14/10/2114/10/24

ASJC Scopus subject areas

  • 情報システム
  • コンピュータ サイエンスの応用

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