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Most previous works on text classification,
represented importance of terms by term occurrence frequency
(tf) and inverse document frequency (idf). This paper presents
the ways to apply class frequency in centroidbased text
categorization. Three approaches are taken into account. The
first one is to explore the effectiveness of inverse class
frequency on the popular term weighting, i.e., TFIDF, as a
replacement of idf and an addition to TFIDF. The second
approach is to evaluate some functions, which are used to
adjust the power of inverse class frequency. The other approach
is to apply terms, which are found in only one class or few
classes, to improve classification performance, using twostep
classification. From the results, class frequency expresses its
usefulness on text classification, especially the twostep
classification.
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