Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
A review of feature selection techniques in bioinformatics
Bioinformatics
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Exploring the boundary region of tolerance rough sets for feature selection
Pattern Recognition
Rough Computing: Theories, Technologies and Applications
Rough Computing: Theories, Technologies and Applications
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction
IEEE Transactions on Knowledge and Data Engineering
On the compact computational domain of fuzzy-rough sets
Pattern Recognition Letters
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
Attributes Reduction Using Fuzzy Rough Sets
IEEE Transactions on Fuzzy Systems
Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Core-generating discretization for rough set feature selection
Transactions on rough sets XIII
Fuzzy-rough feature selection aided support vector machines for Mars image classification
Computer Vision and Image Understanding
Finding rough and fuzzy-rough set reducts with SAT
Information Sciences: an International Journal
Updating attribute reduction in incomplete decision systems with the variation of attribute set
International Journal of Approximate Reasoning
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A recent TRANSACTIONS ON FUZZY SYSTEMS paper proposing a new fuzzy-rough feature selector (FRFS) has claimed that the more attributes remain in datasets, the better the approximations and hence resulting models. [Tsang et al., IEEE Trans. Fuzzy Syst., vol. 16, no. 5, pp. 1130-1141]. This claim has been used as a primary criticism of the original FRFS method [Jensen and Shen, IEEE Trans. Fuzzy Syst., vol. 15, no. 1, pp. 73-89, Feb. 2007]. Although, in certain applications, it may be necessary to consider as many features as possible, the claim is contrary to the motivation behind feature selection concerning the curse of dimensionality, the presence of redundant and irrelevant features, and the large amount of literature documenting observed improvements in modeling techniques following data reduction. This letter discusses this issue, as well as two other issues raised by Tsang et al. [IEEE Trans. Fuzzy Syst., vol. 16, no. 5, pp. 1130-1141, Oct. 2008] regarding the original algorithm.