Abstract
Feature reduction is an important pre-processing step in classification and other artificial intelligent applications. Its aim is to improve the quality of feature sets. There are two main types of feature reduction: feature construction and feature selection. Most current feature reduction algorithms focus on just one of the two types because they require different representations. This paper proposes a new representation which supports a feature reduction algorithm that combines feature selection and feature construction. The algorithm uses new genetic operators to update the new representation. The proposed algorithm is compared with two conventional feature selection algorithms, a genetic algorithms-based feature selection algorithm, and a genetic programming-based algorithm which evolves feature sets containing both original and high-level features. The experimental results on 10 different datasets show that the new representation can help to produce a smaller number of features and improve the classification accuracy over using all features on most datasets. In comparison with other feature selection or construction algorithms, the proposed algorithm achieves similar or better classification performance on all datasets.
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Nguyen, H.B., Xue, B., Andreae, P. (2017). A Hybrid GA-GP Method for Feature Reduction in Classification. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_48
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DOI: https://doi.org/10.1007/978-3-319-68759-9_48
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