Variable precision rough set model
Journal of Computer and System Sciences
Knowledge discovery in databases: an attribute-oriented rough set approach
Knowledge discovery in databases: an attribute-oriented rough set approach
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uncertainly measures of rough set prediction
Artificial Intelligence
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
A Handwritten Numeral Character Classification Using Tolerant Rough Set
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Information Sciences—Informatics and Computer Science: An International Journal
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Journal of the American Society for Information Science and Technology
Multiknowledge for decision making
Knowledge and Information Systems
Entropies of fuzzy indiscrenibility relation and its operations
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On fuzzy-rough sets approach to feature selection
Pattern Recognition Letters
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Constructing rough decision forests
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Fuzzy probabilistic approximation spaces and their information measures
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Consistency based attribute reduction
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Research on rough set theory and applications in China
Transactions on rough sets VIII
Feature subset selection wrapper based on mutual information and rough sets
Expert Systems with Applications: An International Journal
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Feature subset selection and data reduction is a fundamental and most explored area in machine learning and data mining. Rough set theory has been witnessed great success in attribute reduction. A series of reduction algorithms were constructed for all kinds of applications based on rough set models. There is usually more than one reduct for some real world data sets. It is not very clear which one or which subset of the reducts should be selected for learning. Neither experimental comparison nor theoretic analysis was reported so far. In this paper, we will review the proposed attribute reduction algorithms and reduction selection strategies. Then a series of numeric experiments are presented. The results show that, statistically speaking, the classification systems trained with the reduct with the least features get the best generalization power in terms of single classifiers. Furthermore, Good performance is observed from combining the classifiers constructed with multiple reducts compared with Bagging and random subspace ensembles.