A Method of the Rule Acquisition Based on Rough Set
28 Nov 2006-pp 243-243
TL;DR: The basic concept and operation rule of rough set is presented and it is shown that definitive rules about rectifying column are gotten from original data through rough set theory.
Abstract: Rough sets is a mathematical approach to deal with vague, uncertain and imperfect data and it has many advantages over neural network. The basic concept and operation rule of rough set is presented in this paper. As an example to show the advantages, definitive rules about rectifying column are gotten from original data through rough set theory.
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Book•
31 Oct 1991
TL;DR: Theoretical Foundations.
Abstract: I. Theoretical Foundations.- 1. Knowledge.- 1.1. Introduction.- 1.2. Knowledge and Classification.- 1.3. Knowledge Base.- 1.4. Equivalence, Generalization and Specialization of Knowledge.- Summary.- Exercises.- References.- 2. Imprecise Categories, Approximations and Rough Sets.- 2.1. Introduction.- 2.2. Rough Sets.- 2.3. Approximations of Set.- 2.4. Properties of Approximations.- 2.5. Approximations and Membership Relation.- 2.6. Numerical Characterization of Imprecision.- 2.7. Topological Characterization of Imprecision.- 2.8. Approximation of Classifications.- 2.9. Rough Equality of Sets.- 2.10. Rough Inclusion of Sets.- Summary.- Exercises.- References.- 3. Reduction of Knowledge.- 3.1. Introduction.- 3.2. Reduct and Core of Knowledge.- 3.3. Relative Reduct and Relative Core of Knowledge.- 3.4. Reduction of Categories.- 3.5. Relative Reduct and Core of Categories.- Summary.- Exercises.- References.- 4. Dependencies in Knowledge Base.- 4.1. Introduction.- 4.2. Dependency of Knowledge.- 4.3. Partial Dependency of Knowledge.- Summary.- Exercises.- References.- 5. Knowledge Representation.- 5.1. Introduction.- 5.2. Examples.- 5.3. Formal Definition.- 5.4. Significance of Attributes.- 5.5. Discernibility Matrix.- Summary.- Exercises.- References.- 6. Decision Tables.- 6.1. Introduction.- 6.2. Formal Definition and Some Properties.- 6.3. Simplification of Decision Tables.- Summary.- Exercises.- References.- 7. Reasoning about Knowledge.- 7.1. Introduction.- 7.2. Language of Decision Logic.- 7.3. Semantics of Decision Logic Language.- 7.4. Deduction in Decision Logic.- 7.5. Normal Forms.- 7.6. Decision Rules and Decision Algorithms.- 7.7. Truth and Indiscernibility.- 7.8. Dependency of Attributes.- 7.9. Reduction of Consistent Algorithms.- 7.10. Reduction of Inconsistent Algorithms.- 7.11. Reduction of Decision Rules.- 7.12. Minimization of Decision Algorithms.- Summary.- Exercises.- References.- II. Applications.- 8. Decision Making.- 8.1. Introduction.- 8.2. Optician's Decisions Table.- 8.3. Simplification of Decision Table.- 8.4. Decision Algorithm.- 8.5. The Case of Incomplete Information.- Summary.- Exercises.- References.- 9. Data Analysis.- 9.1. Introduction.- 9.2. Decision Table as Protocol of Observations.- 9.3. Derivation of Control Algorithms from Observation.- 9.4. Another Approach.- 9.5. The Case of Inconsistent Data.- Summary.- Exercises.- References.- 10. Dissimilarity Analysis.- 10.1. Introduction.- 10.2. The Middle East Situation.- 10.3. Beauty Contest.- 10.4. Pattern Recognition.- 10.5. Buying a Car.- Summary.- Exercises.- References.- 11. Switching Circuits.- 11.1. Introduction.- 11.2. Minimization of Partially Defined Switching Functions.- 11.3. Multiple-Output Switching Functions.- Summary.- Exercises.- References.- 12. Machine Learning.- 12.1. Introduction.- 12.2. Learning From Examples.- 12.3. The Case of an Imperfect Teacher.- 12.4. Inductive Learning.- Summary.- Exercises.- References.
7,826 citations
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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.
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.
7,185 citations
TL;DR: Basic concepts of rough set theory will be outlined and its possible application will be briefly discussed and further research problems will conclude the paper.
641 citations
"A Method of the Rule Acquisition Ba..." refers background in this paper
...The appearance of Rough Set Theory(RST) supplies us a tool to solve these problems....
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...Because Rough Set Theory could be used to original information directly and it could avoid the trouble of select the training patterns, it is more practicable than neural network....
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...Rough Set Theory is based on the premise that lowering the degree of precision in the data makes the data pattern more visible, whereas the central premise of the rough set philosophy is that the knowledge consists in the ability of classification....
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...Rough Set Theory, which has been developed by Pawlak and his coworkers since the early 1980s, emerged as a major mathematical tool for dealing with inexact, uncertain and vague knowledge....
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...Summing up, we could find the minimal number of condition attributes for pick-up rules from original information when applying Rough Set Theory to rectifying column....
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Journal Article•
TL;DR: A method of fault diagnosis rule acquisition using Rough Set theory can solve the problem of rule acquisition under condition of imprecise and inconsistent information on account of kind overlapping.
Abstract: The purpose of this paper is to present a method of fault diagnosis rule acquisition using Rough Set theory. This method can solve the problem of rule acquisition under condition of imprecise and inconsistent information on account of kind overlapping. Knowledge representation in the form of rules closes to man brain reasoning procedure, so diagnosis method based on rules has a wide application in fault diagnosis, but rule acquisition is one of the bottlenecks. Rough Set (RS) theory has been present for developing automation rule generation system, its main idea is to acquire concept's classifying rules through knowledge reduction maintaining classify ability. Therefore, we can use RS theory to fault diagnosis based on rules. This paper presents a flow chart of rule acquisition based on decision matrix and decision function, explains its application method using a fault diagnosis example, and proves its validity.
1 citations
"A Method of the Rule Acquisition Ba..." refers methods in this paper
...The results of the rough set approach are presented in the form of classification or decision tables derived from a set of original data[2]....
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