Data pre-processing by genetic algorithms for bankruptcy prediction

Chih Fong Tsai, Jui Sheng Chou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Bankruptcy prediction has been approached by data mining techniques. However, since data pre-processing including feature selection or dimensionality reduction and data reduction is a very important stage for successful data mining, very few consider performing both tasks to examine the impact of data pre-processing on prediction performance. This paper applies genetic algorithms, which have been widely used for the data pre-processing tasks, for feature selection and data reduction over a public bankruptcy prediction dataset. In particular, the experiments based on different priorities of performing feature selection and data reduction are conducted. The results show that performing data reduction only can allow the support vector machine (SVM) classifier to provide the highest rate of prediction accuracy. However, executing both feature selection and data reduction with different priorities performs the same. They not only largely reduce the dataset size, but also keep the similar performance as SVM without data pre-processing.

Original languageEnglish
Title of host publicationIEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011
Pages1780-1783
Number of pages4
DOIs
StatePublished - 2011
EventIEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011 - Singapore, Singapore
Duration: 6 Dec 20119 Dec 2011

Publication series

NameIEEE International Conference on Industrial Engineering and Engineering Management
ISSN (Print)2157-3611
ISSN (Electronic)2157-362X

Conference

ConferenceIEEE International Conference on Industrial Engineering and Engineering Management, IEEM2011
Country/TerritorySingapore
CitySingapore
Period6/12/119/12/11

Keywords

  • Bankruptcy prediction
  • data mining
  • data pre-processing genetic algorithms
  • data reduction
  • feature selection

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