Abstract
Feature subset selection has become more and more a common topic of research. This popularity is partly due to the growth in the number of features and application domains. It is of the greatest importance to take themost of every evaluation of the inducer, which is normally the more costly part. In this paper, a technique is proposed that takes into account the inducer evaluation both in the current subset and in the remainder subset (its complementary set) and is applicable to any sequential subset selection algorithm at a reasonable overhead in cost. Its feasibility is demonstrated on a series of benchmark data sets.
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Prat-Masramon, G., Belanche-Muñoz, L.A. (2010). Remainder Subset Awareness for Feature Subset Selection. In: Bramer, M., Ellis, R., Petridis, M. (eds) Research and Development in Intelligent Systems XXVI. Springer, London. https://doi.org/10.1007/978-1-84882-983-1_25
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DOI: https://doi.org/10.1007/978-1-84882-983-1_25
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