abstract

• Typical testors are useful tools for feature selection and for determining feature relevance in supervised classification problems. Nowadays, computing all typical testors of a training matrix is very expensive; all reported algorithms have exponential complexity depending on the number of columns in the matrix. In this paper, we introduce the faster algorithm BR (Boolean Recursive), called fast-BR algorithm, that is based on elimination of gaps and reduction of columns. Fast-BR algorithm is designed to generate all typical testors from a training matrix, requiring a reduced number of operations. Experimental results using this fast implementation and the comparison with other state-of-the-art related algorithms that generate typical testors are presented. © 2013 World Scientific Publishing Company.

authors

• Lias-Rodríguez A.
• Sánchez Díaz Guillermo

publication date

• 2013-01-01

published in

• International Journal of Pattern Recognition and Artificial Intelligence  Journal

keywords

• Feature selection; logical combinatorial pattern recognition; pattern recognition; tipycal testors Algorithms; Feature extraction; Pattern recognition; Exponential complexity; Fast implementation; Feature relevance; Related algorithms; Supervised classification; tipycal testors; Matrix algebra

Digital Object Identifier (DOI)

• 10.1142/S0218001413500225

start page

end page

volume

• 27

issue

• 8