Abstract
Our objective is to provide a comprehensive introduction to Genetic Programming (GP) primarily keeping in view the problem of classifier design along with feature selection. We begin with a brief account of how genetic programming has emerged as a major computational intelligence technique. Then, we analyse classification and feature selection problems in brief. We provide a naive model of GP-based binary classification strategy with illustrative examples. We then discuss a few existing methodologies in brief and three somewhat related but different strategies with reasonable details. Before concluding, we make a few important remarks related to GP when it is used for classification and feature selection. In this context, we show some experimental results with a recent GP-based approach.
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Nag, K., Pal, N.R. (2019). Genetic Programming for Classification and Feature Selection. In: Bansal, J., Singh, P., Pal, N. (eds) Evolutionary and Swarm Intelligence Algorithms. Studies in Computational Intelligence, vol 779. Springer, Cham. https://doi.org/10.1007/978-3-319-91341-4_7
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DOI: https://doi.org/10.1007/978-3-319-91341-4_7
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