An evolutionary algorithm with acceleration operator to generate a subset of typical testors
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Fecha
2014-05-01
Autores
Sánchez-Díaz, Guillermo
Díaz-Sánchez, Germán
Mora-González, Miguel
Aguirre-Salado, Carlos A.
Huerta-Cuéllar, Guillermo
Piza-Dávila, Hugo I.
Reyes-Cárdenas, Óscar
Cárdenas-Tristán, Abraham
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Editor
Elsevier
Resumen
Descripción
This paper is focused on introducing a Hill-Climbing algorithm as a way to solve the problem of generating
typical testors – or non-reducible descriptors – from a training matrix. All the algorithms reported
in the state-of-the-art have exponential complexity. However, there are problems for which there is no
need to generate the whole set of typical testors, but it suffices to find only a subset of them. For this reason,
we introduce a Hill-Climbing algorithm that incorporates an acceleration operation at the mutation
step, providing a more efficient exploration of the search space. The experiments have shown that, under
the same circumstances, the proposed algorithm performs better than other related algorithms reported
so far.
Palabras clave
Hill Climbers, Feature Selection, Typical Testors, Pattern Recognition
Citación
Sanchez-Diaz, G.; Diaz-Sanchez, G.; Mora-Gonzalez, M; Piza-Davila, H.I.; Aguirre-Salado, C.A.; Huerta-Cuellar, G; Reyes-Cardenas, O.; Cardenas-Tristan, A. (2014). "An evolutionary algorithm with acceleration operator to generate a subset of typical testors". Pattern Recognition Letters. Volume 41, 1 May, pp.34-42.