Combination of multiple classifiers for the customer's purchase behavior prediction

Eunju Kim, Wooju Kim, Yillbyung Lee

Research output: Contribution to journalArticlepeer-review

139 Citations (Scopus)


In these days, EC companies are eager to learn about their customers using data mining technologies. But the diverse situations of such companies make it difficult to know which is the most effective algorithm for the given problems. Recently, a movement towards combining multiple classifiers has emerged to improve classification results. In this paper, we propose a method for the prediction of the EC customer's purchase behavior by combining multiple classifiers based on genetic algorithm. The method was tested and evaluated using Web data from a leading EC company. We also tested the validity of our approach in general classification problems using handwritten numerals. In both cases, our method shows better performance than individual classifiers and other known combining methods we tried.

Original languageEnglish
Pages (from-to)167-175
Number of pages9
JournalDecision Support Systems
Issue number2
Publication statusPublished - 2003 Jan

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


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