Fuzzy-Rough Feature Selection Based on λ-Partition Differentiation Entropy

Fuzzy-rough set theory is proven as an effective tool for feature selection. Whilst promising, many state-of-the-art fuzzy-rough feature selection algorithms are time-consuming when dealing with the datasets which have a large quantity of features. In order to address this issue, a lambda-partition differentiation entropy fuzzy-rough feature selection (LDE-FRFS) method is proposed in this paper. Such lambda-partition differentiation entropy extends the concept of partition differentiation entropy from rough sets to fuzzy-rough sets on the view of a partition of the information system. In this case, it can efficiently gauge the significance of features. Experimental results demonstrate that, by such lambda-partition differentiation entropy-based attribute significance, LDE-FRFS outperforms the competitors in terms of both the size of the reduced datasets and the execute time.