Feature Construction, Feature Reduction and Search Space Reduction Using Genetic Programming

Abstract

Feature construction and feature selection are essential pre-processing techniques in data mining, especially for high-dimensional data. The principal goals of such techniques are to increase accuracy in classification tasks and reduce runtime in the learning process. Genetic programming is used to construct a new high-level feature space. Additionally, the feature selection process, immersed in the task, is seized. Therefore, a set of features with relevant information is obtained. This paper presents an approach to reducing the features of high-dimensional data throughout genetic programming. Moreover, reducing the search space eliminates features that do not have considerable information over the generations of the search process. Although the approach is simple, competitive results are achieved. In the implementation, the wrapper approach is used for the classifier to lead the searching process.