C4.5: programs for machine learning
C4.5: programs for machine learning
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
Induction of Strong Feature Subsets
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
A Hybrid Approach to Feature Selection
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Generalized rough sets based feature selection
Intelligent Data Analysis
Feature selection by ordered rough set based feature weighting
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Scalable improved quick reduct: sample based
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
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In this paper, we address the problem of feature subset selection using rough set theory. We propose a scalable algorithm to find a set of reducts based on discernibility function, which is an alternative solution for the exhaustive approach. Our study shows that our algorithm improves the classical one from three points of view: computation time, reducts size and the accuracy of induced model.