Evolutionary feature selection in boosting

The purpose of this study is to clarify the effectiveness of a new type of weak learner in boosting for pattern classification. Our weak learner is called EFS (Evolutionaty Feature Selection). The EFS has two aspects: The first is a feature-subset selector for pattern classification. The EFS selects effective combinations of features using an evolutionary technique. An entropy-based criterion called VQCCE (Vector-quantized Conditional Class Entropy) is used for the evaluation of feature-combinations. The second is a weak learner in boosting. We utilize the vector-quantization in the EFS as the weak learner. In this paper, we apply our method to some benchmark problems and discuss the effectiveness of our method, in comparing with a conventional boosting with C4.5 decision trees.