Feature selection for handwritten Chinese character recognition based on genetic algorithms

Feature selection is of great importance in recognition system design because it directly affects the overall performance of the recognition system. Feature selection can be considered as a problem of global combinatorial optimization. It is a very time-consuming task to search the most suitable features amongst a huge number of possible feature combinations, therefore, an effective and efficient search technique is desired. In this paper, we use genetic algorithms (GA) to design a feature selection approach for handwritten Chinese character recognition. Four contributions are claimed: First, the general transformed divergence among classes, which is derived from Mahalanobis distances, is proposed to be the fitness function in the feature selection based on GA; Second, a special crossover operator other than traditional one is given; Third, a special criterion of terminating selections is inferred from the criterion of minimum error probability in a Bayes classifier; Fourth, we compare our method with the feature selection based on branch-and-bound algorithm (BAB), which is often used to reduce the calculation of feature selection via exhaustive search. The analyses of the experimental results can be proceeded that traditional GA is an ergodic Markov chain, while, BAB is a depth first heuristic algorithm for exhaustive search. We conclude that the GA-based method proposed in this paper is promising to solve the feature selection problems in a multidimensional space.