Abstract
The feature selection problem in the field of classification consists of obtaining a subset of variables to optimally realize the task without taking into account the remainder variables. This work presents how the search for this subset is performed using the Scatter Search metaheuristic and is compared with two traditional strategies in the literature: the Forward Sequential Selection (FSS) and the Backward Sequential Selection (BSS). Promising results were obtained. We use the lazy learning strategy together with the nearest neighbour methodology (NN) also known as Instance-Based Learning Algorithm 1 (IB1).
This work has been partially supported by the project TIC2002-04242-C03-01, 70% of which are FEDER founds.
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García López, F.C., García Torres, M., Moreno Pérez, J.A., Moreno Vega, J.M. (2004). Scatter Search for the Feature Selection Problem. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, JL. (eds) Current Topics in Artificial Intelligence. TTIA 2003. Lecture Notes in Computer Science(), vol 3040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25945-9_51
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DOI: https://doi.org/10.1007/978-3-540-25945-9_51
Publisher Name: Springer, Berlin, Heidelberg
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