- Abstract:
-
One of the main obstacles facing current intelligent pattern recognition applica- tions is that of dataset dimensionality. To enable these systems to be eective, 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 quantization 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-based, PCA-based and random-based methods.
- Copyright:
- 2004 by The University of Edinburgh. All Rights Reserved
- Links To Paper
- No links available
- Bibtex format
- @Article{EDI-INF-RR-0198,
- author = {
Qiang Shen
and Richard Jensen
},
- title = {Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring},
- journal = {Pattern Recognition},
- year = 2004,
- month = {Mar},
- }
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