Study of category score algorithms for k-NN classifier

Authors:
Huaizhong KOU;Georges Gardarin
Affiliations:
PRiSM Laboratory , University of Versailles, Versailles, France;e-XMLMedia, BOURG LA REINE, France
Venue:
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2002

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Abstract

We analyzes category score algorithms for k-NN classifier found in the literature, including majority voting algorithm (MVA), simple sum algorithm (SSA). MVA and SSA are two mainly used algorithms to estimate score for candidate categories in k-NN classifier systems. Based on the hypothesis that utilization of internal relation between documents and categories could improve system performance, two new weighting score models: concept-based weighting (CBW) score model and term independence-based weighting (IBW) score model are proposed. Our experimental results confirm our hypothesis and show that in the term of precision average IBW and CBW are better than the other score models, while SSA is higher than MVA. According to macro-average F1 CBW performs best. Rocchio-based algorithm (RBA) always performs worst.