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 λ-partition differentiation entropy fuzzy-rough feature selection (LDE-FRFS) method is proposed in this paper. Such λ-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 λ-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.
|Title of host publication||2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)|
|Number of pages||6|
|Publication status||E-pub ahead of print - 25 Jun 2018|
|Event||2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) - Guilin|
Duration: 29 Jul 2017 → 31 Jul 2017
|Conference||2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)|
|Period||29/07/17 → 31/07/17|