Alternative Strategies to Explore the SNNB Algorithm Performance

Data mining is the process of extracting useful knowledge from large datasets. A subarea of data mining is the classification that induces a set of models for predicting the label of the unknown class. The Naive Bayes classifier is simple, efficient and robust; its performance has been improved by some works, which focused on finding an instances subset in a conditional way and selecting the appropriate classifier with the highest probability. In this paper we propose to modify the Selective Neighborhood based Naive Bayes (SNNB) algorithm, using and combining other distance measurements, instance organization, instance space search and model selection. The proposed combinations are aimed at exploring the classifying accuracy of the SNNB algorithm. Experimental results show that the best strategy found (using 26 datasets from the UCI repository) won in 15 cases and only lost in 3 cases