Fuzzy Sets and Rough Sets for Scenario Modelling and Analysis
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Are more features better? a response to attributes reduction using fuzzy rough sets
IEEE Transactions on Fuzzy Systems
The Knowledge Engineering Review
Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
A bit-chain based algorithm for problem of attribute reduction
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
International Journal of Approximate Reasoning
A novel feature selection method and its application
Journal of Intelligent Information Systems
Knowledge reduction for decision tables with attribute value taxonomies
Knowledge-Based Systems
Multi-level rough set reduction for decision rule mining
Applied Intelligence
An improved algorithm for calculating fuzzy attribute reducts
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Feature Selection (FS) or Attribute Reduction techniques are employed for dimensionality reduction and aim to select a subset of the original features of a data set which are rich in the most useful information. The benefits of employing FS techniques include improved data visualization and transparency, a reduction in training and utilization times and potentially, improved prediction performance. Many approaches based on rough set theory up to now, have employed the dependency function, which is based on lower approximations as an evaluation step in the FS process. However, by examining only that information which is considered to be certain and ignoring the boundary region, or region of uncertainty, much useful information is lost. This paper examines a rough set FS technique which uses the information gathered from both the lower approximation dependency value and a distance metric which considers the number of objects in the boundary region and the distance of those objects from the lower approximation. The use of this measure in rough set feature selection can result in smaller subset sizes than those obtained using the dependency function alone. This demonstrates that there is much valuable information to be extracted from the boundary region. Experimental results are presented for both crisp and real-valued data and compared with two other FS techniques in terms of subset size, runtimes, and classification accuracy.