Naïve Bayes Classifier with Parallel Abduction Reasoning Ensemble Principal Component Analysis for Prediction Modeling

Abstract

The classification could be a latent variable that is probabilistically relating to the discovered variables. In Bayesian algorithmic ways, logical thinking works in probabilistic mode. However PCM based parallel abductive reasoning with Naïve Bayes (NB) on cancer information could be a powerful technique to perform effective prediction in classification. Whereas whilst classifying the cancer information the strategy reads the parallel changes and predicts the severity level for supplementary treatments. Since the Bayesian classifier gives many premises for several supervised learning algorithms thereby the proposed Parallel abductive Naïve Bayes Classifier algorithm based on factor analysis of PCA enhances the granularity of prediction. The Principal components are chosen on multi-perspective domain of curator analysis dataset. Experimental result shows that it is potential to get parallel abductive classifiers that have comparatively high impact on prediction.