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2017 | OriginalPaper | Chapter

Fast-BR vs. Fast-CT_EXT: An Empirical Performance Study

Authors : Vladímir Rodríguez-Diez, José Fco. Martínez-Trinidad, J. Ariel Carrasco-Ochoa, Manuel S. Lazo-Cortés

Published in: Pattern Recognition

Publisher: Springer International Publishing

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Abstract

Testor Theory allows performing feature selection in supervised classification problems through typical testors. Typical testors are irreducible subsets of features preserving the object discernibility ability of the original set of features. However, finding the complete set of typical testors for a dataset requires a high computational effort. In this paper, we make an empirical study about the performance of two of the most recent and fastest algorithms of the state of the art for computing typical testors, regarding the density of the basic matrix. For our study we use synthetic basic matrices to control their characteristics, but we also include public standard datasets taken from the UCI machine learning repository. Finally, we discuss our conclusions drawn from this study.

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previous chapter Modeling Dependencies in Supervised Classification

next chapter Assessing Deep Learning Architectures for Visualizing Maya Hieroglyphs

The source code as well as all the basic matrices and datasets used in our experiments can be downloaded from http://ccc.inaoep.mx/~ariel/CTBRES.

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Title: Fast-BR vs. Fast-CT_EXT: An Empirical Performance Study
Authors: Vladímir Rodríguez-Diez
José Fco. Martínez-Trinidad
J. Ariel Carrasco-Ochoa
Manuel S. Lazo-Cortés
Publisher: Springer International Publishing
Book: Pattern Recognition
Print ISBN: 978-3-319-59225-1

Electronic ISBN: 978-3-319-59226-8

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-59226-8_13

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