Abstract
This paper proposes a novel approach to search for the optimal combination of a measure function and feature weights using an evolutionary algorithm. Different combinations of measure function and feature weights are used to construct the searching space. Genetic Algorithm is applied as an evolutionary algorithm to search for the candidate solution, in which the classification rate of the K-Nearest Neighbor classifier is used as the fitness value. Three experiments are carefully designed to show the attractiveness of our approach. In the first experiment, an artificial data set is constructed to verify the effectiveness of the proposed approach by testing whether it could find the optimal combination of measure function and feature weights which satisfy the data set. In the second experiment, data sets from the University of California at Irvine are employed to verify the general applicability of the method. Finally, a prostate cancer data set is used to show its effectiveness on high-dimensional data.
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© 2012 Springer-Verlag Berlin Heidelberg
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Wang, S., He, S., Zhu, H. (2012). Similarity Measurement and Feature Selection Using Genetic Algorithm. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_3
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DOI: https://doi.org/10.1007/978-3-642-31362-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31361-5
Online ISBN: 978-3-642-31362-2
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