Comparison of Naive Basian and K-NN Classifier
Deepak Kanojia, Mahak Motwani · International Journal of Computer Applications · 2013
In this paper comparison is done between k-nearest neighbor and naive basin classifier based on the subset of features. Sequential feature selection method is used to establish the subsets. Four categories of subsets are used like life and medical transcripts, arts and humanities transcripts, social science transcripts, physical science transcripts to show the experimental results to classify data and to show that K-NN classifier gets competition with naive basian classifier. The classification performance K-NN classifier is far better then naive basian classifier when learning parameters and number of samples are small. But as the number of samples increases the naive basian classifier performance is better K-NN classifier. On the other hand naive basian classifier is much better then K-NN classifier when computational demand and memory requirements are considered. This paper demonstrates the strength of naive basian classifier for classification and summarizes the some of the most important developments in naive basian classification and K- nearest neighbor classification research. Specifically, the issues of posterior probability estimation, the link between Naive basian and K-NN classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. The purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics. General Terms