Toward high accuracy and visualization: An interpretable feature extraction method based on genetic programming and non-overlap degree

Genetic programming (GP) has shown promising results in interpretable feature extraction, but few works considered both classification accuracy and data visualization as objectives. Evaluating the extracted features based on the combination of accuracy measures and visualization measures can help to achieve the two objectives simultaneously. However, the exploitation of improper visualization measures and combination methods will decrease the classification accuracy. In this paper, a novel feature extraction method based on GP and non-overlap degree is proposed to extract interpretable features for high accuracy and visualization. And a novel function that maximizes the product of the accuracy of a linear classifier and the non-overlap degree is proposed to evaluate the extracted features. The proposed method, named GP-ANO, is compared with other methods on five medical datasets by six common machine learning methods. The experimental results demonstrate that the GP-ANO method outperforms other compared methods in terms of both classification accuracy and data visualization.