A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function

Jae Pil Hwang, Seongkeun Park, Euntai Kim

Research output: Contribution to journalArticle

63 Citations (Scopus)

Abstract

In this paper, a new weighted approach on Lagrangian support vector machine for imbalanced data classification problem is proposed. The weight parameters are embedded in the Lagrangian SVM formulation. The training method for weighted Lagrangian SVM is presented and its convergence is proven. The weighted Lagrangian SVM classifier is tested and compared with some other SVMs using synthetic and real data to show its effectiveness and feasibility.

Original languageEnglish
Pages (from-to)8580-8585
Number of pages6
JournalExpert Systems with Applications
Volume38
Issue number7
DOIs
Publication statusPublished - 2011 Jul

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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