Abstract

Web page classification is one of the essential techniques for Web mining. Specifically, classifying Web pages of a user-interesting class is the first step of mining interesting information from the Web. However, constructing a classifier for an interesting class requires laborious pre-processing such as collecting positive and negative training examples. For instance, in order to construct a "homepage" classifier, one needs to collect a sample of homepages (positive examples) and a sample of non-homepages (negative examples). In particular, collecting negative training examples requires arduous work and special caution to avoid biasing them. We introduce in this paper the Positive Example Based Learning (PEBL) framework for Web page classification which eliminates the need for manually collecting negative training examples in pre-processing. We present an algorithm called Mapping-Convergence (M-C) that achieves classification accuracy (with positive and unlabeled data) as high as that of traditional SVM (with positive and negative data). Our experiments show that when the M-C algorithm uses the same amount of positive examples as that of traditional SVM, the M-C algorithm performs as well as traditional SVM.

Original languageEnglish (US)
Pages239-248
Number of pages10
StatePublished - Dec 1 2002
EventKDD - 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Edmonton, Alta, Canada
Duration: Jul 23 2002Jul 26 2002

Other

OtherKDD - 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryCanada
CityEdmonton, Alta
Period7/23/027/26/02

Keywords

  • Labeled data
  • Mapping-convergence (M-C) algorithm
  • SVM (support vector machine)
  • Unlabeled data

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Yu, H., Han, J., & Chang, K. C. C. (2002). PEBL: Positive example based learning for web page classification using SVM. 239-248. Paper presented at KDD - 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alta, Canada.