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

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Engineering, Eastern Mediterranean University, Famagusta, Northern Cyprus

    Hakan Altınçay & Zafer Erenel

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  1. Hakan Altınçay
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  2. Zafer Erenel
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Correspondence to Hakan Altınçay.

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Altınçay, H., Erenel, Z. Using the absolute difference of term occurrence probabilities in binary text categorization. Appl Intell 36, 148–160 (2012). https://doi.org/10.1007/s10489-010-0250-3

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