Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring |
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Authors: | Qiang Shen Richard Jensen |
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Affiliation: | School of Informatics, The University of Edinburgh, Centre for Intelligent Systems and their Applications, Appleton Tower, Crichton Street, Edinburgh EH8 9LE, UK |
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Abstract: | One of the main obstacles facing current intelligent pattern recognition applications is that of dataset dimensionality. To enable these systems to be effective, a redundancy-removing step is usually carried out beforehand. Rough set theory (RST) has been used as such a dataset pre-processor with much success, however it is reliant upon a crisp dataset; important information may be lost as a result of quantisation of the underlying numerical features. This paper proposes a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, to avoid this information loss. The current work retains dataset semantics, allowing for the creation of clear, readable fuzzy models. Experimental results, of applying the present work to complex systems monitoring, show that fuzzy-rough selection is more powerful than conventional entropy-, PCA- and random-based methods. |
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Keywords: | Feature selection Feature dependency Fuzzy-rough sets Reduct search Rule induction Systems monitoring |
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