Hybrid multi-selection based on swarm intelligence and decision tree
To study the use of swarm intelligence (SI) to integrate feature selection and instance selection for pre-process data,and to boost the prediction accuracy of classifier on the reduced data in data mining (DM),this article puts forward a novel hybrid data pre-processing method based on SI and decision tree.The method uses the binary particle swarm optimization (PSO) as a subset generator to control the particles searching the optimal feature subset and instance subset,and employs a decision tree as a wrapper classifier to evaluate feature subsets and instance subsets at the same time.In the method,the PSO algorithm takes the multi-selection problem as a combinational optimization problem with a reasonable computational cost,and makes full use of the advantages of the two kinds of data pre-processing methods to reduce the data from feature and instance dimensions,and to generate a more optimized classifier on the reduced data.Experiments show the method has attained lower prediction error ratio.Meantime,an average of feature reduction ratio 50.8% and an average of instance reduction ratio 36.8% are obtained at the same time on seven data sets from the University of California,Irvine (UCI),indicting the availability of the method.
swarm intelligence decision tree feature selection instance selection binary particle swarm optimization data pre-processing
XIONG Wen JIN Yao-hong
Beijing Normal University,Institute of Chinese Information Processing,Beijing 100875,China
国内会议
黄山
英文
128-134
2012-10-25（万方平台首次上网日期，不代表论文的发表时间）