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

Eunju Kim, Wooju Kim, Yillbyung Lee

Research output: Contribution to journalArticle

106 Citations (Scopus)

Abstract

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
Volume34
Issue number2
DOIs
Publication statusPublished - 2003 Jan 1

Fingerprint

Classifiers
Industry
Data Mining
Data mining
Genetic algorithms
Technology
Purchase behavior
Prediction
Classifier
World Wide Web
Case method
Genetic algorithm
Individual performance
Case Method
Numerals
Genetic Algorithm

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Information Systems and Management

Cite this

@article{42f60cff5fbd4c5fa61b0409072acf2c,
title = "Combination of multiple classifiers for the customer's purchase behavior prediction",
abstract = "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.",
author = "Eunju Kim and Wooju Kim and Yillbyung Lee",
year = "2003",
month = "1",
day = "1",
doi = "10.1016/S0167-9236(02)00079-9",
language = "English",
volume = "34",
pages = "167--175",
journal = "Decision Support Systems",
issn = "0167-9236",
publisher = "Elsevier",
number = "2",

}

Combination of multiple classifiers for the customer's purchase behavior prediction. / Kim, Eunju; Kim, Wooju; Lee, Yillbyung.

In: Decision Support Systems, Vol. 34, No. 2, 01.01.2003, p. 167-175.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Combination of multiple classifiers for the customer's purchase behavior prediction

AU - Kim, Eunju

AU - Kim, Wooju

AU - Lee, Yillbyung

PY - 2003/1/1

Y1 - 2003/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0037209499&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037209499&partnerID=8YFLogxK

U2 - 10.1016/S0167-9236(02)00079-9

DO - 10.1016/S0167-9236(02)00079-9

M3 - Article

VL - 34

SP - 167

EP - 175

JO - Decision Support Systems

JF - Decision Support Systems

SN - 0167-9236

IS - 2

ER -