In text classification, term weighting methods design appropriate weights to the given terms to improve the text classification performance. Traditional algorithm of term weighting only considers about tf (term frequency), idf (inverse document frequency) and so on, and this approach simply thinks low frequency terms are important, high frequency terms are unimportant, so it designs higher weights to the rare terms frequently. In this paper, we present an effective term weighting approach to avoid the deficiency of the traditional approach, and make use of kNN classifiers to classify over widely-used benchmark data set Reuters-21578. The experimental results prove that the new approach can improve the accuracy of classification.
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