Clonal Selection Classifier with Data Reduction: Classification as an optimization task

This study proposes a new algorithm for supervised learning, based on the clonal selection principle exhibited in natural and artificial immune systems. The method, called Clonal Selection Classifier with Data Reduction (CSCDR), utilizes a fitness function based on the number of correct and incorrect pattern classifications made by each antibody. The algorithm tries to maximize this value through clonal selection processes such as mutation, affinity maturation and selection of the best individuals, transforming the training phase in an optimization problem. This leads to antibodies with more representativeness and thus decreases the amount of prototypes generated at the end of the algorithm. Experimental results on benchmark datasets of the UCI machine learning repository demonstrated the effectiveness of the CSCDR algorithm as a classification technique, combined with a considerable data reduction when compared to the results obtained by the well known Artificial Immune Recognition System (AIRS) and the original Clonal Selection Classifier Algorithm (CSCA).