Variable precision rough set model
Journal of Computer and System Sciences
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Data mining based on rough sets
Data mining
Probabilistic rough set approximations
International Journal of Approximate Reasoning
Probabilistic approach to rough sets
International Journal of Approximate Reasoning
The investigation of the Bayesian rough set model
International Journal of Approximate Reasoning
A Local Version of the MLEM2 Algorithm for Rule Induction
Fundamenta Informaticae - Understanding Computers' Intelligence Celebrating the 100th Volume of Fundamenta Informaticae in Honour of Helena Rasiowa
Generalized parameterized approximations
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Local and global approximations for incomplete data
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Generalized probabilistic approximations of incomplete data
International Journal of Approximate Reasoning
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In this paper we introduce a generalization of the local approximation called a local probabilistic approximation. Our novel idea is associated with a parameter (probability) α. If α = 1, the local probabilistic approximation becomes a local lower approximation; for small α, it becomes a local upper approximation. The main objective of this paper is to test whether proper local probabilistic approximations (different from local lower and upper approximations) are better than ordinary local lower and upper approximations. Our experimental results, based on ten-fold cross validation, show that all depends on a data set: for some data sets proper local probabilistic approximations are better than local lower and upper approximations; for some data sets there is no difference, for yet other data sets proper local probabilistic approximations are worse than local lower and upper approximations.