An evolutionary algorithm with acceleration operator to generate a subset of typical testors

Sánchez-Díaz, Guillermo; Diaz-Sanchez, German; Mora-González, Miguel; Piza-Davila, Ivan; Aguirre-Salado, Carlos Arturo; Huerta-Cuéllar, G.; Reyes-Cárdenas, Oscar; Tristán, Abraham Cárdenas

doi:10.1016/j.patrec.2013.11.006

Cited by 11 publications

(1 citation statement)

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“…On the other hand, heuristics take polinomial time in a fixed number of generations. Some state-of-the-art heuristics include the following: HC [38] and PHC [26]. For large and/or dense datasets, these heuristics have shown very good performance.…”

Section: Related Workmentioning

confidence: 99%

An Algorithm for Computing Minimum-Length Irreducible Testors

et al. 2020

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In pattern recognition, the elimination of unnecessary and/or redundant attributes is known as feature selection. Irreducible testors have been used to perform this task. An objective of the Minimum Description Length Principle (MDL) applied to feature selection in pattern recognition and data mining is to select the minimum number of attributes in a data set. Consequently, the MDL principle leads us to consider the subset of irreducible testors of minimum length. Some algorithms that find the whole set of irreducible testors have been reported in the literature. However, none of these algorithms was designed to generate only minimum-length irreducible testors. In this paper, we propose the first algorithm specifically designed to calculate all minimum-length irreducible testors from a training sample. The paper presents some experimental results obtained using synthetic and real data in which the performance of the proposed algorithm is contrasted with other state-of-the-art algorithms that were adapted to generate only irreducible testers of minimum length. INDEX TERMS Feature selection, MDL principle, minimum-length irreducible testors, testor.

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Section: Related Workmentioning

confidence: 99%