A study of selective neighborhood-based naive Bayes for efficient lazy learning

This work studies two accuracy estimation techniques, global accuracy estimation and local accuracy estimation, under the algorithmic framework of the selective neighborhood-based naive Bayes (SNNB) for lazy classification, resulting in two concrete learning algorithms of linear computational complexity, SNNB-G and SNNB-L. Extensive experiments show that SNNB-L is more accurate than naive Baye, C4.5, and SNNB-G.