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.