Error correcting output codes (ECOC) is a powerful tool to solve multi-classification problem. The existing methods of seeking the optimal coding matrix ignore the correlation between classes

which leads to bad performance in learning speed and classification accuracy. In order to construct data-driven coding matrix

an optimization strategy of coding matrix based on immune clonal selection algorithm (ICSA) is presented. The strategy reduces the multiple constraints non-deterministic polynomial problem (NP) of finding the optimal coding matrix to a finite heuristic search problem. Firstly

the affinity function based on accuracy and coding length is defined. Then

the mutation

crossover and selection operator are modified respectively. Meanwhile

the validity constraints are combined to execute the quick search. The experiment results based on UCI and traffic data prove that the proposed strategy can enhance the classification performance and accelerated the speed. The output coding matrix is more compact as well.