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• W1573450826 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." @default.
• W1573450826 created "2016-06-24" @default.
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• W1573450826 date "2010-01-01" @default.
• W1573450826 modified "2023-09-26" @default.
• W1573450826 title "Feature Set Reduction by Evolutionary Selection and Construction" @default.
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• W1573450826 doi "https://doi.org/10.1007/978-3-642-13541-5_15" @default.
• W1573450826 hasPublicationYear "2010" @default.
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