Dynamic feature selection with fuzzy-rough sets

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

10 Citations (SciVal)


Various strategies have been exploited for the task of feature selection, in an effort to identify more compact and better quality feature subsets. Most existing approaches focus on selecting from a static pool of training instances with a fixed number of original features. However, in practice, data may be gradually refined, and information regarding the problem domain may be actively added or removed. In this paper, a technique based on fuzzy-rough sets is extended to support dynamic feature selection. The proposed method is capable of carrying out on-line selection with incrementally added features or instances. Also, the cases of feature or instance removal are investigated. This brings a novel and beneficial addition to the current research in feature selection. Four possible dynamic selection scenarios are considered, with algorithms proposed in order to handle such individual situations. Simulated experimentation is carried out using real world benchmark data sets, in order to demonstrate the efficacy of the proposed work.
Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Fuzzy Systems
PublisherIEEE Press
Number of pages7
ISBN (Print)978-1-4799-0020-6
Publication statusPublished - 2013
EventFuzzy Systems - Hyderabad, Hyderabad, India
Duration: 07 Jul 201310 Jul 2013
Conference number: 22


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2013
Period07 Jul 201310 Jul 2013


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