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<Paper uid="N04-4002">
  <Title>MMR-based feature selection for text categorization</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We introduce a new method of feature selection for text categorization. Our MMR-based feature selection method strives to reduce redundancy between features while maintaining information gain in selecting appropriate features for text categorization. Empirical results show that MMR-based feature selection is more effective than Koller &amp; Sahami's method, which is one of greedy feature selection methods, and conventional information gain which is commonly used in feature selection for text categorization. Moreover, MMR-based feature selection sometimes produces some improvements of conventional machine learning algorithms over SVM which is known to give the best classification accuracy.</Paragraph>
  </Section>
class="xml-element"></Paper>