Journal ArticleDOI
Dominance-based rough set approach and knowledge reductions in incomplete ordered information system
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This paper attempts to present research focusing on a complex incomplete information system-the incomplete ordered information system, where all attributes are considered as criterions and classification analysis in such incomplete information systems is conducted.About:
This article is published in Information Sciences.The article was published on 2008-02-20. It has received 175 citations till now. The article focuses on the topics: Dominance-based rough set approach & Complete information.read more
Citations
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Journal ArticleDOI
Attribute selection with fuzzy decision reducts
TL;DR: This paper introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure, and presents a generalization of the classical rough set framework for data-based attribute selection and reduction using fuzzy tolerance relations.
Journal ArticleDOI
Three-way recommender systems based on random forests
Heng-Ru Zhang,Fan Min +1 more
TL;DR: Experimental results on the well-known MovieLens data set show that the (α*, β*)-pair determined by three-way decision is optimal not only on the training set, but also on the testing set.
Journal ArticleDOI
Variable-precision dominance-based rough set approach and attribute reduction
TL;DR: An advantage of VP-DRSA over variable-consistency dominance-based rough set approach in decision rule induction is emphasized and some relations among the VP- DRSA-based attribute reduction approaches are investigated.
Journal ArticleDOI
Stochastic dominance-based rough set model for ordinal classification
TL;DR: A probabilistic model for ordinal classification problems with monotonicity constraints is introduced and the equivalence of the variable consistency rough sets to the specific empirical risk-minimizing decision rule in the statistical decision theory is shown.
Journal ArticleDOI
Dominance-based rough set approach to incomplete interval-valued information system
TL;DR: The purpose of this paper is to further investigate the dominance-based rough set in incomplete interval-valued information system, which contains both incomplete and imprecise evaluations of objects.
References
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Lecture Notes in Artificial Intelligence
P. Brezillon,P. Bouquet +1 more
TL;DR: The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.
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
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.
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.
Journal ArticleDOI
Rough set approach to incomplete information systems
TL;DR: This work proposes reduction of knowledge that eliminates only that information, which is not essential from the point of view of classification or decision making, and shows how to find decision rules directly from such an incomplete decision table.