Effect of term distributions on centroid-based text categorization
Most of traditional text categorization approaches utilize term frequency (tf) and inverse document frequency (idf) for representing importance of words and/or terms in classifying a text document. This paper describes an approach to apply term distributions, in addition to tf and idf, to improve performance of centroid-based text categorization. Three types of term distributions, called inter-class, intra-class and in-collection distributions, are introduced. These distributions are useful to increase classification accuracy by exploiting information of (1) term distribution among classes, (2) term distribution within a class and (3) term distribution in the whole collection of training data. In addition, this paper investigates how these term distributions contribute to weight each term in documents, e.g., a high term distribution of a word promotes or demotes importance or classification power of that word. To this end, several centroid-based classifiers are constructed with different term weightings. Using various data sets, their performances are investigated and compared to a standard centroid-based classifier (TDIDF) and a centroid-based classifier modified with information gain. Moreover, we also compare them to two well-known methods: k-NN and na??ve Bayes. In addition to a unigram model of document representation, a bigram model is also explored. Finally, the effectiveness of term distributions to improve classification accuracy is explored with regard to the training set size and the number of classes.
term distribution text categorization centroid-based classifier