Abstract: Focusing on the time-consuming problem of wrapper feature selection when the feature subset is evaluated using high-complexity classifiers in pattern recognition, a novel parallel immune clonal selection for feature selection algorithm (PICFS) is proposed. The presented method uses an immune clonal selection for feature selection; fitness of feature subset fitness is determined by evaluating the nearest neighbor classifier with leave-one-out cross-validation in multiple computing nodes at the same time. Experimental results on several standards UCI dataset sets show that the proposed algorithm outperforms the conventional genetic algorithm and classical sequential floating forward search algorithm in terms of classification accuracy and greatly reduce the running time based on MPICH using the Linux blade cluster, we have achieved a speed-up as high as 29.57 even when up to 40 processors are used.

Key words: pattern recognition, parallel algorithms, feature selection, classification