featureselection is an important task in data mining, which aims to reduce the dimensionality of the data sets while at least maintaining the classification performance. Chicken swarm optimization algorithm (CSO) has...
详细信息
ISBN:
(纸本)9781538619315
featureselection is an important task in data mining, which aims to reduce the dimensionality of the data sets while at least maintaining the classification performance. Chicken swarm optimization algorithm (CSO) has been widely applied to featureselection because of its efficiency and effectiveness. However, since featureselection is a challenging task with a complex search space, CSO quickly gets stuck the local minimum problem. This paper aims to improve the CSO searching ability by applying logistic and tend chaotic maps to assist the CSO swarm in exploring the search space better. The proposed chaotic chicken swarm algorithm (CCSO)-based featureselection algorithm is compared with four featureselection algorithms on five benchmark data sets. A comparison among several types of popular classifiers is done to figure out the sensitivity of each classifier corresponding to the selected features and the dimension reduction. During iterations, the best fitness value shows remarkable improvement of the classification accuracy.
暂无评论