Using the absolute difference of term occurrence probabilities in?binary text categorization |
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Authors: | Hakan Alt?n?ay Zafer Erenel |
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Affiliation: | (1) The Electronic and Information Engineering School, Xi’an Jiaotong University, Xi’an, China;(2) Internet Education School, Xi’an Jiaotong University, Xi’an, China;(3) MOE KLINNS Lab and SPKLSTN Lab, Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China |
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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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Keywords: | |
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