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We present a general rule induction algorithm based on sequential covering, suitable for variable consistency rough set approaches. This algorithm, called VC-DomLEM, can be used f...
Jerzy Blaszczynski, Roman Slowinski, Marcin Szelag
A naive Bayesian classifier is a probabilistic classifier based on Bayesian decision theory with naive independence assumptions, which is often used for ranking or constructing a...
Feature subset selection presents a common challenge for the applications where data with tens or hundreds of features are available. Existing feature selection algorithms are mai...
A scenario in ontology development and its use is hypothesis testing, such as finding new subconcepts based on the data linked to the ontology. During such experimentation, knowle...
: We propose a new feature selection strategy based on rough sets and Particle Swarm Optimization (PSO). Rough sets has been used as a feature selection method with much success, b...
Xiangyang Wang, Jie Yang, Xiaolong Teng, Weijun Xi...
The main statistics used in rough set data analysis, the approximation quality, is of limited value when there is a choice of competing models for predicting a decision variable. ...
In this paper, we discuss an approach to structural objects based on a generalisation of indiscernibility relation used in rough set theory. The existing results in rough set theor...
In the paper we explore the idea of describing Pawlak's rough sets using three-valued logic, whereby the value t corresponds to the positive region of a set, the value f -- to...
Rough set theory can be applied to rule induction. There are two different types of classification rules, positive and boundary rules, leading to different decisions and consequen...
This paper reviews probabilistic approaches to rough sets in granulation, approximation, and rule induction. The Shannon entropy function is used to quantitatively characterize pa...