Abstract
The selection of features that describe samples in sets of data is a typical problem in data mining. A crucial issue is to select a maximal set of pertinent features, because the scarce knowledge of the problem under study often leads to consider features which do not provide a good description of the corresponding samples. The concept of consistent biclustering of a set of data has been introduced to identify such a maximal set. The problem can be modeled as a 0–1 linear fractional program, which is NP-hard. We reformulate this optimization problem as a bilevel program, and we prove that solutions to the original problem can be found by solving the reformulated problem. We also propose a heuristic for the solution of the bilevel program, that is based on the meta-heuristic Variable Neighborhood Search (VNS). Computational experiments show that the proposed heuristic outperforms previously proposed heuristics for feature selection by consistent biclustering.
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Mucherino, A., Liberti, L. (2013). A VNS-Based Heuristic for Feature Selection in Data Mining. In: Talbi, EG. (eds) Hybrid Metaheuristics. Studies in Computational Intelligence, vol 434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30671-6_13
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DOI: https://doi.org/10.1007/978-3-642-30671-6_13
Publisher Name: Springer, Berlin, Heidelberg
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