Error-Correcting Output Codes as a unifying framework for studying the multiclass categorization problems can reduce them to multiple binary problems effectively

thus simplifying the problem.But when generating component classifiers

we usually need to face the contradiction between the diversity among the component classifiers and the consistency of learning between the component classifiers and the ensemble classifiers.We call this contradiction consistent-diverse balance problem.How to reduce the error ratio caused by the inconsistency under diversity big enough is the breakthrough of the balance problem.Using weighted decoding

we can reduce the classification error caused by the learning inconsistency through relearning for weight coefficient matrix.In the proposed algorithm

by using GA to learn the weight coefficient matrix and taking the final generalization error of the ensemble classifiers as the fitness function

we can get the weight coefficient matrix of which the error of the training samples is minimum.The experiments respectively on artificial data sets and UCI data sets have proved that the algorithm is better than others for the consistent-diverse balance problem.