Using the absolute difference of term occurrence probabilities in binary text categorization

In this study, the differences among widely used weighting schemes are studied by means of ordering terms according to their discriminative abilities using a recently developed framework which expresses term weights in terms of the ratio and absolute difference of term occurrence probabilities. Having observed that the ordering of terms is dependent on the weighting scheme under concern, it is emphasized that this can be explained by the way different schemes use term occurrence differences in generating term weights. Then, it is proposed that the relevance frequency which is shown to provide the best scores on several datasets can be improved by taking into account the way absolute difference values are used in other widely used schemes. Experimental results on two different datasets have shown that improved F-1 scores can be achieved.