A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Information Sciences—Informatics and Computer Science: An International Journal
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
A novel statistical method on decision table analysis
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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Rough set based rule induction methods have been applied to knowledge discovery in databases, whose empirical results obtained show that they are very powerful and that some important knowledge has been extracted from datasets. However, quantitative evaluation of lower and upper approximation are based not on statistical evidence but on rather naive indices, such as conditional probabilities and functions of conditional probabilities. In this paper, we introduce a new approach to induced lower and upper approximation of original and variable precision rough set model for quantitative evaluation, which can be viewed as a statistical test for rough set methods. For this extension, chi-square distribution, F-test and likelihood ratio test play an important role in statistical evaluation. Chi-square test statistic measures statistical information about an information table and F-test statistic and likelihood ratio statistic are used to measure the difference between two tables.