Discriminative feature analysis and selection for document classification

  • Authors:
  • Punya Murthy Chinta;M. Narasimha Murty

  • Affiliations:
  • Computer Science and Automation, Indian Institute of Science, Bangalore, India;Computer Science and Automation, Indian Institute of Science, Bangalore, India

  • Venue:
  • ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
  • Year:
  • 2012

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Abstract

Classification of a large document collection involves dealing with a huge feature space where each distinct word is a feature. In such an environment, classification is a costly task both in terms of running time and computing resources. Further it will not guarantee optimal results because it is likely to overfit by considering every feature for classification. In such a context, feature selection is inevitable. This work analyses the feature selection methods, explores the relations among them and attempts to find a minimal subset of features which are discriminative for document classification.