Rough set algorithms in classification problem
Rough set methods and applications
Combining Pattern Classifiers: Methods and Algorithms
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Approximation Degrees in Decision Reduct-Based MRI Segmentation
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
Probabilistic approach to rough sets
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Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
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International Journal of Approximate Reasoning
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The investigation of the Bayesian rough set model
International Journal of Approximate Reasoning
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RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
On reduct construction algorithms
Transactions on computational science II
Rough-set-inspired feature subset selection, classifier construction, and rule aggregation
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Ensembles of bireducts: towards robust classification and simple representation
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
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We propose a framework for experimental verification whether mechanisms of voting among rough-set-based classifiers and criteria for extracting those classifiers from data should follow analogous mathematical principles. Moreover, we show that some of types of criteria perform better for high-quality data while the others are useful rather for low-quality data. The framework is based on the principles of approximate attribute reduction and probabilistic extensions of rough-set-based approach to data analysis. The framework is not supposed to produce the best-ever classification results, unless it is extended by some additional parameters known from the literature. Instead, our major goal is to illustrate in a possibly simplistic way that it is worth unifying mathematical background for the stages of learning and applying rough-set-based classifiers.