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

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.