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Emergence of feature extraction function using genetic programming

A novel method of feature extraction to improve the performance of pattern recognition is proposed. It is assumed that the feature consists of a polynomial expression of the original patterns. The term of polynomial expressions are searched by genetic programming. In order to evaluate the effectiveness of the proposed method, we apply the k nearest neighbor classifier as the classification algorithm. Experiments were performed for an artificial task and an acoustic diagnosis for compressors as the real world task. From these results, we confirmed that the proposed method was effective for the feature extraction.