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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.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.m MISCELLANEOUS

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Classifier design and evaluation

General Terms:
Algorithms, Experimentation, Performance

Keywords:
Mapping-Convergence (M-C) algorithm, SVM (Support Vector Machine), labeled data, unlabeled data