An Ensemble Associated Feature Subset Selection for Classification Problems

Feature subset selection is an important problem in machine learning and data mining. If the suitable features are selected, the results of classification or prediction will be more accurate; while if the unsuitable features are used, the results may have no meaningful. This paper presents a method for feature subset selection that uses the ensemble technique to increase the efficiency of feature selection. Association rule mining is introduced to select the high relationship features. Bagging concept is applied to increase the confidence of selection. The experimental results show the efficiency of the proposed method that outperforms the efficiency of simple association feature subset selection.