Feature selection based on bagging ensemble learning algorithm

Generalization ability is a principal issue in the field of machine learning. Feature selection is a method that can improve generalization ability of learning algorithm. Through measuring feature count measure (FCM) in decision table, select the feature which depended strongly on classification attribute. Based on the above, Feature count measure based bagging ensemble learning algorithm is proposed. Experiment results show that the proposed algorithm is effective to obtain classification rule.