Abstract
Together with increasing sizes of collected data, the problem of feature set reduction becomes more important. Machine learning methods, including classifiers, are sensitive to the training data. One of the known problems is called ’curse of dimensionality’. Some features (attributes) in the collection of data may not be informative so they obstruct the learning process. Removing them is very desirable from the classification quality point of view. In the paper we focus on wrapper approach to feature set reduction. We propose an evolutionary method to feature reduction by means of selection and construction. Genetic Algorithm is used as a tool for feature selection and Gene Expression Programming as a tool of dimensionality reduction by features construction. The paper presents the approach and the results of conducted experiments. Conclusions and future plans end the paper.
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Drozdz, K., Kwasnicka, H. (2010). Feature Set Reduction by Evolutionary Selection and Construction. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2010. Lecture Notes in Computer Science(), vol 6071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13541-5_15
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DOI: https://doi.org/10.1007/978-3-642-13541-5_15
Publisher Name: Springer, Berlin, Heidelberg
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