FSVM-CIL: Fuzzy support vector machines for class imbalance learning

Rukshan Batuwita, Vasile Palade

Research output: Contribution to journalArticle

230 Citations (Scopus)

Abstract

Support vector machines (SVMs) is a popular machine learning technique, which works effectively with balanced datasets. However, when it comes to imbalanced datasets, SVMs produce suboptimal classification models. On the other hand, the SVM algorithm is sensitive to outliers and noise present in the datasets. Therefore, although the existing class imbalance learning (CIL) methods can make SVMs less sensitive to class imbalance, they can still suffer from the problem of outliers and noise. Fuzzy SVMs (FSVMs) is a variant of the SVM algorithm, which has been proposed to handle the problem of outliers and noise. In FSVMs, training examples are assigned different fuzzy-membership values based on their importance, and these membership values are incorporated into the SVM learning algorithm to make it less sensitive to outliers and noise. However, like the normal SVM algorithm, FSVMs can also suffer from the problem of class imbalance. In this paper, we present a method to improve FSVMs for CIL (called FSVM-CIL), which can be used to handle the class imbalance problem in the presence of outliers and noise. We thoroughly evaluated the proposed FSVM-CIL method on ten real-world imbalanced datasets and compared its performance with five existing CIL methods, which are available for normal SVM training. Based on the overall results, we can conclude that the proposed FSVM-CIL method is a very effective method for CIL, especially in the presence of outliers and noise in datasets.

Original languageEnglish
Article number5409611
Pages (from-to)558-571
Number of pages14
JournalIEEE Transactions on Fuzzy Systems
Volume18
Issue number3
DOIs
Publication statusPublished - Jun 2010
Externally publishedYes

Keywords

  • Class imbalance learning (CIL)
  • Fuzzy support vector machines (FSVMs)
  • Outliers
  • Support vector machines (SVMs)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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