Fuzzy measures in determining seed points in clustering
Pattern Recognition Letters
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An introduction to variable and feature selection
The Journal of Machine Learning Research
Consistency-based search in feature selection
Artificial Intelligence
Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis
IEEE Transactions on Knowledge and Data Engineering
Redundancy in Feature Extraction
IEEE Transactions on Computers
Feature Selection with a Linear Dependence Measure
IEEE Transactions on Computers
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Fundamenta Informaticae
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation
Transactions on Rough Sets IX
Effective feature selection scheme using mutual information
Neurocomputing
Feature Selection Using f-Information Measures in Fuzzy Approximation Spaces
IEEE Transactions on Knowledge and Data Engineering
Fuzzy-rough sets for information measures and selection of relevant genes from microarray data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Rough sets for selection of molecular descriptors to predict biological activity of molecules
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
International Journal of Approximate Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-dependent rough-fuzzy granular space, dispersion index and classification
Pattern Recognition
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough Set Based Generalized Fuzzy -Means Algorithm and Quantitative Indices
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy–Rough Supervised Attribute Clustering Algorithm and Classification of Microarray Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy probabilistic approximation spaces and their information measures
IEEE Transactions on Fuzzy Systems
On the selection and classification of independent features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging
Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging
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Attribute selection is one of the important problems encountered in pattern recognition, machine learning, data mining, and bioinformatics. It refers to the problem of selecting those input attributes or features that are most effective to predict the sample categories. In this regard, rough set theory has been shown to be successful for selecting relevant and nonredundant attributes from a given data set. However, the classical rough sets are unable to handle real valued noisy features. This problem can be addressed by the fuzzy-rough sets, which are the generalization of classical rough sets. A feature selection method is presented here based on fuzzy-rough sets by maximizing both relevance and significance of the selected features. This paper also presents different feature evaluation criteria such as dependency, relevance, redundancy, and significance for attribute selection task using fuzzy-rough sets. The performance of different rough set models is compared with that of some existing feature evaluation indices based on the predictive accuracy of nearest neighbor rule, support vector machine, and decision tree. The effectiveness of the fuzzy-rough set based attribute selection method, along with a comparison with existing feature evaluation indices and different rough set models, is demonstrated on a set of benchmark and microarray gene expression data sets.