Supervised immune clonal evolutionary classification algorithm for high-dimensional data

Classification is very difficult in high-dimensional spaces because learning methods suffer from the curse of dimensionality. In order to efficiently classify the high-dimensional data, a Supervised Immune Clonal Evolutionary Classification Algorithm (SICECA) is proposed in this paper. First, the automatic nonparametric Uncorrelated Discriminant Analysis (UDA) is adopted for Dimensionality Reduction (DR), which combines rank-preserving dimensionality reduction with constraint discriminant analysis so as to realize the extracted features statistically uncorrelated. Then, an Immune Clonal Evolutionary Algorithm (ICEA) based on clonal selection principle in immunology is proposed to act as classifier. In the experiments, first of all, 11 UCI data sets, four texture images and three Synthetic Aperture Radar (SAR) images are used to test the performance of SICECA. SICECA is also compared with three existing algorithms in terms of classification accuracy and running time. In addition, SICECA is applied to a real world application, namely, face recognition, with a good performance obtained.