不平衡数据分类的混合算法-控制理论与应用.PDF

28 10 Vol. 28 No. 10 2011 10 Control Theory Applications Oct. 2011 : 1000−8152(2011)10−1485−05 , (, 116024) : , , . , 95%; Bagging, , . UCI, G , . : ; ; ; : TP751 : A Hybrid algorithm for classification of unbalanced datasets HAN Min, ZHU Xin-rong (Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian Liaoning 116024, China) Abstract: A novel hybrid algorithm of radial basis function neural network(RBFNN) integrated with the random forest algorithm is proposed to improve the poor classification result produced by traditional algorithm in classifying minor class of unbalanced datasets. Firstly, random interpolations are inserted between adjacent data in the minor dataset to balance the data distribution. Receiver operator characteristics(ROC) with degree of confidence less than 95% are considered the redundant characteristic and are deleted. The input data are perturbed by the Bagging technique. Radial Basis Function Neural Network is employed to be the basic classifier in the random forest. The fusion of decisions and the outputs are determined by the vast majority of votes. This method is applied to UCI dataset. The precision of G-mean and the area under the ROC demonstrate the improvement of the accuracy in the classifications of medium-size unbalanced and large- size unbalance class data sets. Key words: imbalanced data; random forest; radial basis function neural network(RBFNN); receiver operator charac- teristics(ROC) 1 (Introduction) [5] [6]; 2) . , , [7] ; 3) , ,