Model for measuring accuracies of majority voting of ensemble classifier with COB and genetic algorithm

Ensemble learning is a technique to improve the performance and accuracy of classification and predication of machine learning algorithm. Many researchers proposed a model for ensemble classifier for merging a different classification algorithm, but the performance of ensemble algorithm suffered from problem of outlier, noise and core point problem of data from features selection process. In this paper we combined core, outlier and noise data (COB) for features selection process for ensemble model. The process of best feature selection with appropriate classifier used genetic algorithm. Empirical results with UCI data set prediction on Ecoil and glass dataset indicate that the proposed COB model optimization algorithm can help to improve accuracy and classification.