Fuzzy rough feature selection based on OWA aggregation of fuzzy relations

Pan Su, Changjing Shang, Yitian Zhao, Tianhua Chen, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


The interaction between features, or attributes, of a dataset forms a major topic in machine learning and data mining. In particular, a wide range of methods have been established for feature selection, ranking, and grouping. Amongst these, fuzzy rough set based feature selection (FRFS) has been shown to be highly effective at reducing dimensionality for real-valued datasets while retaining attribute semantics. In fuzzy rough sets, the concept of crisp equivalence classes is extended by fuzzy similarity relations, and real-valued similarity measures can be captured between data instances in terms of their attribute values. Therefore, it is desirable to study the aggregation of fuzzy similarity relations to reflect the interactions between attributes. This paper presents an approach that employs OWA aggregation of fuzzy similarity relations to better perform FRFS. A high degree of modelling flexibility is provided by choosing the stress function in OWA. Experimental studies demonstrate that through using different stress functions, different features may be selected; and that given an appropriate stress function, the quality of selected features can improve over that achievable by the state-of-the-art FRFS, in performing classification tasks.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISBN (Electronic)9781509060344
ISBN (Print) 9781509060351
Publication statusPublished - 24 Aug 2017
Externally publishedYes
EventIEEE International Conference on Fuzzy Systems - Royal Continental Hotel, Naples, Italy
Duration: 9 Jul 201712 Jul 2017
http://www.fuzzieee2017.org/ (Link to Conference Website)


ConferenceIEEE International Conference on Fuzzy Systems
Abbreviated titleFUZZ-IEEE 2017
Internet address


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