Modified naïve bayes classifier for e-catalog classification

Authors:
Young-gon Kim;Taehee Lee;Jonghoon Chun;Sang-goo Lee
Affiliations:
School of Computer Science and Engineering / Center for E-Business Research, Seoul National University, Seoul, Republic of Korea;School of Computer Science and Engineering / Center for E-Business Research, Seoul National University, Seoul, Republic of Korea;Department of Computer Engineering, Myongji University, Yongin, Kyunggi-Do, Republic of Korea;School of Computer Science and Engineering / Center for E-Business Research, Seoul National University, Seoul, Republic of Korea
Venue:
DEECS'06 Proceedings of the Second international conference on Data Engineering Issues in E-Commerce and Services
Year:
2006

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Abstract

As the wide use of online business transactions, the volume of product information that needs to be managed in a system has become drastically large, and the classification task of such data has become highly complex. The heterogeneity among competing standard classification schemes makes the problem only harder. However, the classification task is an indispensable part for successful e-commerce applications. In this paper, we present an automated approach for e-catalog classification. We extend the Naïve Bayes Classifier to make use of the structural characteristics of e-catalogs. We show how we can improve the accuracy of classification when appropriate characteristics of e-catalogs are utilized. Effectiveness of the proposed methods is validated through experiments.