An algorithm for computing minimum-length irreducible testors Article

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Overview

abstract

• 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. © 2020 IEEE.

authors

• Lazo-Cortes M.
• Piza-Dávila I.
• Sánchez Díaz Guillermo
• Villalón-Turrubiates I.

publication date

• 2020-01-01

published in

• IEEE Access  Journal

Research

keywords

• Feature selection; MDL principle; Minimum-length irreducible testors; Testor Data mining; Data set; MDL principle; Minimum description length principle; Minimum-length irreducible testors; State-of-the-art algorithms; Synthetic and real data; Testor; Training sample; Feature extraction

Identity

Digital Object Identifier (DOI)

• 10.1109/ACCESS.2020.2982133

Additional Document Info

start page

• 56312

end page

• 56320

volume

• 8