abstract

• In pattern recognition, irreducible testors have been used for feature selection. A number of exhaustive algorithms that find irreducible testors have been reported in the literature. One of the latest and more efficient algorithms reported is YYC, an incremental algorithm that finds all the irreducible testors from a training matrix. Its efficiency relies on building a smaller number of feature combinations by finding compatible sets from the top of the matrix to the current row. Nevertheless, as the number of sets currently found grows, YYC execution becomes too slow. This work proposes two improvements of YYC algorithm, incorporated in a pre-processing phase; additionally, a parallel version is implemented. The paper presents some experimental results using synthetic and real data. © 2018 World Scientific Publishing Company.

authors

• Lazo-Cortes M.
• Noyola Medrano María Cristina
• Piza-Dávila I.
• Sánchez Díaz Guillermo

publication date

• 2018-01-01

published in

• International Journal of Pattern Recognition and Artificial Intelligence  Journal

keywords

• feature selection; Irreducible testors; pattern recognition; supervised classification Feature extraction; Pattern recognition; Feature combination; Incremental algorithm; Irreducible testors; Its efficiencies; Parallel version; Pre-processing; Supervised classification; Synthetic and real data; Matrix algebra

Digital Object Identifier (DOI)

• 10.1142/S0218001418600017

start page

end page

volume

• 32

issue

• 1