Variable precision rough set model
Journal of Computer and System Sciences
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A machine program for theorem-proving
Communications of the ACM
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection for Clustering
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Feature Selection with a Linear Dependence Measure
IEEE Transactions on Computers
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
A New Approach to Fuzzy-Rough Nearest Neighbour Classification
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Feature selection with fuzzy decision reducts
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Fuzzy Rough Sets: The Forgotten Step
IEEE Transactions on Fuzzy Systems
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
Unsupervised fuzzy-rough set-based dimensionality reduction
Information Sciences: an International Journal
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For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors to reflect decision class labels. It is therefore intuitive to retain only those features that are related to or lead to these decision classes. However, in unsupervised learning, decision class labels are not provided, which poses questions such as; which features should be retained? and, why not use all of the information? The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, some new fuzzy-rough set-based approaches to unsupervised feature selection are proposed. These approaches require no thresholding or domain information, can operate on real-valued data, and result in a significant reduction in dimensionality whilst retaining the semantics of the data.