Fast unsupervised feature selection based on the improved binary ant system and mutation strategy

The “curse of dimensionality” issue caused by high-dimensional datasets not only imposes high memory and computational costs but also deteriorates the capability of learning methods. The main purpose of feature selection is to reduce the dimensionality of these datasets by discarding redundant and irrelevant features, which improves the performance of the learning algorithm. In this paper, a new feature selection algorithm, referred to as...