Abstract
Feature selection plays an important role in pattern recognition and smart computing. The full set of typical testors constitutes a useful tool for solving feature selection problems, especially those problems in which the objects are described by both quantitative and qualitative features. However, finding the typical testors involves a high computational cost. That is why even the most efficient methods become unsuitable to solve some problems. In this work, a new algorithm was introduced in order to reduce the long runtimes involved in the search of typical testors. The performance of the proposed algorithm was evaluated by means of several tests, which use both real-world and simulation data. MATLAB and Java language on Eclipse SDK platform were used to build the simulation dataset and to perform the tests, respectively. The runtimes achieved by the proposed algorithm were significantly shorter than those obtained by fast-BR and GCreduct (the two fastest algorithms) mainly when the latter ones exhibited excessively long runtimes.
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