A Hybrid APSO-aided Learnable Bayesian Classifier

In this paper a hybrid adaptive particle swarm optimization aided learnable Bayesian classifier is proposed. The objective of the classifier is to solve some of the fundamental problems associated with the pure naive Bayesian classifier and its variants with a larger view towards maximization of the classifier accuracy. Further, the proposed algorithm can exhibits an improved capability to eliminate spurious features from large datasets aiding researchers in identifying those features that are solely responsible for achieving higher classification accuracy. The effectiveness of the proposed algorithm is demonstrated on several benchmark datasets.