An Ensemble Method based on Particle of Swarm for the Reduction of Noise, Outlier and Core Point

The majority voting and accurate prediction ofclassification algorithm in data mining arechallenging task for data classification. For theimprovement of data classification used differentclassifier along with another classifier in a mannerof ensembleprocess. Ensemble process increase theclassification ratio of classification algorithm, nowsuch par diagram of classification algorithm iscalled ensemble classifier. Ensemble learning is atechnique to improve the performance and accuracyof classification and predication of machinelearning algorithm. Many researchers proposed amodel for ensemble classifier for merging adifferent classification algorithm, but theperformance of ensemble algorithm suffered fromproblem of outlier, noise and core pointproblem ofdata from features selection process. In this paperwe combined core, outlier and noise data (COB) forfeatures selection process for ensemble model. Theprocess of best feature selection with appropriateclassifier used particle of swarm optimization.