Journal Article10.1145/219717.219791
Rough sets
7.2K
TL;DR: 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.
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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.
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Citations
A combined forecasting approach based on fuzzy soft sets
TL;DR: The experimental results show that the combined approach provided in this paper improves the forecasting performance of each individual forecast and is free from a rough sets approach's restrictions as well, a promising forecasting approach and a new application of soft sets theory.
281
Decision-making with distance measures and induced aggregation operators
TL;DR: The induced ordered weighted averaging distance (IOWAD) operator is a new aggregation operator that extends the OWA operator by using distance measures and a reordering of arguments that depends on order-inducing variables.
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Granular Computing - The Emerging Paradigm
Witold Pedrycz
- 01 Jan 2007
TL;DR: An overview of Granular Computing is provided - a rapidly growing area of information processing aimed at the construction of intelligent systems and the concept of granular modeling is discussed.
278
On the Extension of Rough Sets under Incomplete Information
Jerzy Stefanowski,Alexis Tsoukiàs +1 more
- 09 Nov 1999
TL;DR: Two generalizations of rough set theory are introduced, based on non symmetric similarity relations, while the second one uses valued tolerance relation, which provides more informative results than the previously known approach employing simple tolerance relation.
277
Selecting Discrete and Continuous Features Based on Neighborhood Decision Error Minimization
Qinghua Hu,Witold Pedrycz,Daren Yu,Jun Lang +3 more
- 01 Feb 2010
TL;DR: A novel feature evaluation measure, called the neighborhood decision error rate (NDER), is proposed, which is applicable to both categorical and numerical features and minimizes the classification complexity of the selected feature subset.
272
References
•Book
Rough Sets: Theoretical Aspects of Reasoning about Data
Zdzisław Pawlak
- 31 Oct 1991
TL;DR: Theoretical Foundations.
8.8K
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.
2K
•Book
Advances in the Dempster-Shafer theory of evidence
Ronald R. Yager,Janusz Kacprzyk,Mario Fedrizzi +2 more
- 01 Jan 1994
TL;DR: The Dempster-Shafer Theory of Evidence is applied as a guide for the management of uncertainty in knowledge-based systems.
1K
•Book
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Roman Słowiński
- 01 Aug 1992
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.
917
Rough set approach to multi-attribute decision analysis
TL;DR: The methodology of the rough set analysis of multi-attribute decision problems, depending on the structure of their representation, its interpretation and the kind of questions related, is reviewed.
534
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