Fuzzy-rough feature selection based on λ-partition differentiation entropy

Sun, Qian, Qu, Yanpeng, Deng, Ansheng and Yang, Longzhi (2017) Fuzzy-rough feature selection based on λ-partition differentiation entropy. In: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, pp. 1222-1227. ISBN 978-1-5386-2166-0

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/FSKD.2017.8392938


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.

Item Type: Book Section
Uncontrolled Keywords: Feature selection, Fuzzy-rough sets, λ-Partition differentiation entropy
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 25 Jul 2018 14:43
Last Modified: 11 Oct 2019 19:45
URI: http://nrl.northumbria.ac.uk/id/eprint/35116

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