An Improved Clonal Selection Classifier Incorporating Fuzzy Clustering
Inspired by complementary strategies, a hybrid supervised artificial immune classifier is put forward, which is on the basis of the clonal selection principle, and combined with the Fuzzy C-Means clustering (FCM) algorithm. With the help of FCM clustering, the initial antibodies that image features of data set are extracted effectively, and then a clonal selection algorithm named CLONALG is adopted for each training instance to constitute the memory cells. Finally, classification is performed in a K-Nearest Neighbor approach with the developed set of memory cells. Experimental results on five benchmark datasets from UCI machine learning repository demonstrate the effectiveness of the algorithm as a classification technique. Compared with general CLOALG algorithm for classification, the hybrid classifier not only decrease the computational time, but also can generate less memory cells without sacrificing classification accuracy.
clonal selection fuzzy c-means supervised learning data classification
Gang Li Jian Zhuang Hongning Hou Dehong Yu
School of Mechanical Engineering Xian Jiaotong University Xian 710049, China
国际会议
张家界
英文
179-182
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）