Improvement of Prediction Accuracy Using Discretization and Voting Classifier

There are many examples of classification algorithms developed so far for data analysis, pattern recognition, scene analysis and learning from graphical models. Being motivated by the works of a number of researchers, here the author have tried to improve the prediction accuracy by first discretizing the real world dataset and then applying a voting classifier on the discretized dataset. In this work, continuous dataset from the raw real world dataset having missing attribute values have been generated and discretized the dataset using SPID 3 algorithm. Then naive-Bayesian classifier has been implemented to apply it on the continuous and discretized dataset. Finally, an ensemble learner (Ada-boost algorithm) has been developed where the naive Bayesian classifier has been used as the base learner of the ensemble. The extensive empirical results over the twenty real world datasets show that the prediction accuracy can be increased by the joint performance of discretization and voting classifier