Journal ArticleDOI
Rough sets
TLDR
This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.Abstract:
Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.read more
Citations
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Journal ArticleDOI
Wrappers for feature subset selection
Ron Kohavi,George H. John +1 more
TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.
Journal ArticleDOI
Data mining: an overview from a database perspective
TL;DR: In this paper, a survey of the available data mining techniques is provided and a comparative study of such techniques is presented, based on a database researcher's point-of-view.
Journal ArticleDOI
Soft set theory
TL;DR: The authors define equality of two soft sets, subset and super set of a soft set, complement of asoft set, null soft set and absolute soft set with examples and De Morgan's laws and a number of results are verified in soft set theory.
Journal ArticleDOI
Rudiments of rough sets
Zdziasław Pawlak,Andrzej Skowron +1 more
TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.
Journal ArticleDOI
Rough sets theory for multicriteria decision analysis
TL;DR: The original rough set approach proved to be very useful in dealing with inconsistency problems following from information granulation, but is failing when preference-orders of attribute domains (criteria) are to be taken into account and it cannot handle inconsistencies following from violation of the dominance principle.
References
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Journal ArticleDOI
Variable precision rough set model
TL;DR: A generalized model of rough sets called variable precision model (VP-model), aimed at modelling classification problems involving uncertain or imprecise information, is presented and the main concepts are introduced formally and illustrated with simple examples.
Book
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
TL;DR: The use of 'Rough Sets' Methods to draw Premonitory Factors for Earthquakes by emphasising Gas Geochemistry: The Case of a Low Seismic Activity Context in Belgium J.T. Polkowski is used.
BookDOI
Intelligent Decision Support
TL;DR: The paper presents the system LERS for rule induction, which computes lower and upper approximations of each concept and induces certain rules and possible rules that can be induced from the input data.
Book ChapterDOI
LERS-A System for Learning from Examples Based on Rough Sets
TL;DR: The paper presents the system LERS for rule induction, which handles inconsistencies in the input data due to its usage of rough set theory principle and induces all rules, each in the minimal form, that can be induced from the inputData.