Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients

Maciej Ziȩba*, Jakub M. Tomczak, Marek Lubicz, Jerzy Świa̧tek

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In this paper, we present boosted SVM dedicated to solve imbalanced data problems. Proposed solution combines the benefits of using ensemble classifiers for uneven data together with cost-sensitive support vectors machines. Further, we present oracle-based approach for extracting decision rules from the boosted SVM. In the next step we examine the quality of the proposed method by comparing the performance with other algorithms which deal with imbalanced data. Finally, boosted SVM is used for medical application of predicting post-operative life expectancy in the lung cancer patients.

Original languageEnglish
Pages (from-to)99-108
Number of pages10
JournalApplied Soft Computing Journal
Volume14
Issue numberPART A
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Boosted SVM
  • Decision rules
  • Imbalanced data
  • Post-operative life expectancy prediction

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