Nonlinear Feature Extraction Using Evolutionary Algorithm

We propose a method of nonlinear feature extraction for 2-class problems. A simple sigmoid function is used to extract features that are negatively correlated to each other. To evaluate the effectiveness of the proposed method, we employ linear and non-linear support vector machines to classify using the extracted feature sets and the original feature sets. Comparison on 4 datasets shows that our method is effective for nonlinear feature extraction.

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