Study of category score algorithms for k-NN classifier

: This paper analyzes category score algorithms for k-NN classifier found in the literature, including majority voting algorithm (MVA), simple sum algorithm (SSA), etc. 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, we propose two new weighting score models: concept-based weighting (CBW) score model and term independence-based weighting (IBW) score model. Our experimental results confirms our hypothesis and show that IBW is better than other score models, CBW and SSA and MVA are complementary, SSA and MVA are mutual. Rocchio-based algorithm (RBA), perform worst.

• Type: Journal
• Year: 2002
• Where: SIGIR
• Authors: Huaizhong Kou, Georges Gardarin
• Keywords: Algorithm, Information Technology, K-nn Classifier, Score Models, SIGIR 2002