  Feature selection and attribute reduction are crucial problems, and widely
used techniques in the field of machine learning, data mining and pattern
recognition to overcome the well-known phenomenon of the Curse of
Dimensionality, by either selecting a subset of features or removing unrelated
ones. This paper presents a new feature selection method that efficiently
carries out attribute reduction, thereby selecting the most informative
features of a dataset. It consists of two components: 1) a measure for feature
subset evaluation, and 2) a search strategy. For the evaluation measure, we
have employed the fuzzy-rough dependency degree (FRFDD) in the lower
approximation-based fuzzy-rough feature selection (L-FRFS) due to its
effectiveness in feature selection. As for the search strategy, a new version
of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The new
feature selection method is obtained by hybridizing the B-SFLA with the FRDD.
Non-parametric statistical tests are conducted to compare the proposed approach
with several existing methods over twenty two datasets, including nine high
dimensional and large ones, from the UCI repository. The experimental results
demonstrate that the B-SFLA approach significantly outperforms other
metaheuristic methods in terms of the number of selected features and the
classification accuracy.
