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.

Typical testors are very useful in Pattern Recognition, especially for Feature Selection problems. The complexity of computing all typical testors of a training matrix has an exponential growth with respect to the number of features. Several methods that speed up the calculation of the set of all typical testors have been developed, but nowadays, there are still problems where this set is impossible to find. With this aim, a new external scale algorithm BR is proposed. The experimental results demonstrate that this method clearly outperforms the two best algorithms reported in the literature.