Abstract
Among recent topics studied in context of feature selection the hybrid algorithms seem to receive particular attention. In this paper we propose a new hybrid algorithm, the flexible hybrid floating sequential search algorithm, that combines both the filter and wrapper search principles. The main benefit of the proposed algorithm is its ability to deal flexibly with the quality-of-result versus computational time trade-off and to enable wrapper based feature selection in problems of higher dimensionality than before. We show that it is possible to trade significant reduction of search time for negligible decrease of the classification accuracy. Experimental results are reported on two data sets, WAVEFORM data from the UCI repository and SPEECH data from British Telecom.
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© 2006 Springer-Verlag Berlin Heidelberg
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Somol, P., Novovičová, J., Pudil, P. (2006). Flexible-Hybrid Sequential Floating Search in Statistical Feature Selection. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_69
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DOI: https://doi.org/10.1007/11815921_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37236-3
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