Analysis of inverse class frequency in centroid-based text classification

Most previous works on text categorization applied term occurrence frequency and inverse document frequency for representing importance of terms. This work presents an analysis of inverse class frequency in centroid-based text categorization. There are two aims of this paper. The first one is to find appropriate functions of inverse class frequency. The other is to find the key factors for using inverse class frequency. The experimental results show that the key factors, which improve classification accuracy, are the numbers of few-class terms and most-class terms. When large numbers of few-class terms and most-class terms are obtained, the logarithmic function of inverse class frequency is the most effective when it is combined with term frequency. The square root of inverse class frequency incorporated into TFIDF, works well in the case when data sets include a small number of few-class terms and most-class terms. To increase the numbers of these effective terms, some methods are involved i.e. using higher gram models, small number of classes and large number of training sets.