Feature extraction using evolutionary computation

We propose a method of feature extraction to improve the performance of pattern recognition. The extracted features are assumed to be a polynomial expression of the original patterns. The polynomial expressions are searched by the genetic programming. In order to evaluate the effectiveness of the proposed method, we apply k nearest neighbor classifier as the classification algorithm. Experiments were performed for two artificial tasks 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