Rule Extraction and Rule Evaluation Based on Granular Computing

2010 ◽  
pp. 196-242
Author(s):  
Jiye Liang ◽  
Yuhua Qian ◽  
Deyu Li
Keyword(s):  
Decision Rule ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Rule Extraction ◽  
Decision Table ◽  
Rule Set ◽  
Decision Tables ◽  
Rule Evaluation

In rough set theory, rule extraction and rule evaluation are two important issues. In this chapter, the concepts of positive approximation and converse approximation are first introduced, which can be seen as dynamic approximations of target concepts based on a granulation order. Then, two algorithms for rule extraction called MABPA and REBCA are designed and applied to hierarchically generate decision rules from a decision table. Furthermore, to evaluate the whole performance of a decision rule set, three kinds of measures are proposed for evaluating the certainty, consistency and support of a decision-rule set extracted from a decision table, respectively. The experimental analyses on several decision tables show that these three new measures are adequate for evaluating the decision performance of a decision-rule set extracted from a decision table in rough set theory. The measures may be helpful for determining which rule extraction technique should be chosen in a practical decision problem.

Analysis of Time-Series Data by Merging Decision Rules

2017 ◽  
Vol 21 (6) ◽  
pp. 1026-1033
Author(s):  
Yoshiyuki Matsumoto ◽  
Junzo Watada ◽  
◽  
Keyword(s):  
Time Series ◽  
Decision Rule ◽  
Set Theory ◽  
Rough Set ◽  
Time Series Data ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Series Data ◽  
Rule Set ◽  
Analysis Of Time Series

Rough set theory was proposed by Z. Pawlak in 1982. This theory enables the mining of knowledge granules as decision rules from a database, the web, and other sources. This decision rule set can then be used for data analysis. We can apply the decision rule set to reason, estimate, evaluate, or forecast an unknown object. In this paper, rough set theory is used for the analysis of time-series data. We propose a method to acquire rules from time-series data using regression. The trend of the regression line can be used as a condition attribute. We predict the future slope of the time-series data as decision attributes. We also use merging rules to further analyze the time series data.

scholarly journals Some algorithms related to consistent decision table

2017 ◽  
Vol 33 (2) ◽  
pp. 131-142
Author(s):  
Quang Minh Hoang ◽  
Vu Duc Thi ◽  
Nguyen Ngoc San
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Polynomial Time ◽  
Time Complexity ◽  
Rough Set Theory ◽  
Decision Rules ◽  
The Other ◽  
Decision Table ◽  
Mathematical Tool ◽  
The Given

Rough set theory is useful mathematical tool developed to deal with vagueness and uncertainty. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. Rough set theory is also the most popular for generating decision rules from decision table. In this paper, we propose an algorithm finding object reduct of consistent decsion table. On the other hand, we also show an algorithm to find some attribute reducts and the correctness of our algorithms is proof-theoretically. These our algorithms have polynomial time complexity. Our finding object reduct helps other algorithms of finding attribute reducts become more effectively, especially as working with huge consistent decision table.

scholarly journals Quality of classification with LERS system in the data size context

2018 ◽  
Vol 16 (1/2) ◽  
pp. 29-38 ◽  
Author(s):  
M. Sudha ◽  
A. Kumaravel
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Research Trend ◽  
Decision Tables ◽  
New Situation ◽  
Decision State

Rough set theory is a simple and potential methodology in extracting and minimizing rules from decision tables. Its concepts are core, reduct and discovering knowledge in the form of rules. The decision rules explain the decision state to predict and support the new situation. Initially it was proposed as a useful tool for analysis of decision states. This approach produces a set of decision rules involves two types namely certain and possible rules based on approximation. The prediction may highly be affected if the data size varies in larger numbers. Application of Rough set theory towards this direction has not been considered yet. Hence the main objective of this paper is to study the influence of data size and the number of rules generated by rough set methods. The performance of these methods is presented through the metric like accuracy and quality of classification. The results obtained show the range of performance and first of its kind in current research trend.

Research on Rough Set Theory-Based Customer Division

2014 ◽  
Vol 989-994 ◽  
pp. 1536-1540
Author(s):  
Gui Juan Song ◽  
Xin Cao ◽  
Xin Yue Wang
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Decision Rules ◽  
Main Idea ◽  
Rule Extraction

The main idea of rough set theory is to extract decision rules by attribute reduction and value reduction in the premises of keeping the ability of classification. This paper presents the design of model for customer division based on rough set, and uses algorithms for attribute reduction and rule extraction in rough set to analyze the customer of supermarket. This paper also introduces how to achieve the minimum result of attribute reduction and decision-making via decision-making report, winkling redundant attribute and over-rule of decision.

scholarly journals An Efficient Algorithm for Determining the Set of All Reductive Attributes in Incomplete Decision Tables

2013 ◽  
Vol 13 (4) ◽  
pp. 118-126
Author(s):  
Janos Demetrovics ◽  
Vu Duc Thi ◽  
Nguyen Long Giang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Polynomial Time ◽  
Efficient Algorithm ◽  
Rough Set Theory ◽  
Decision Table ◽  
Decision Tables

Abstract In rough set theory, the number of all reducts for a given decision table can be exponential with respect to the number of attributes. This paper investigates the problem of determining the set of all reductive attributes which are present in at least one reduct of an incomplete decision table. We theoretically prove that this problem can be solved in polynomial time. This result shows that the problem of determining the union of all reducts can be solved in polynomial time, and the problem of determining the set of all redundant attributes which are not present in any reducts can also be solved in polynomial time.

Application of Rough Set Theory to Rule‐type Knowledge Discovery from Field Inspection Data on Highway Bridges

2017 ◽  
Author(s):  
Ayaho Miyamoto
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Highway Bridges ◽  
Decision Table ◽  
Field Inspection ◽  
Rule Type ◽  
Inspection Data ◽  
Contradictory Data

This paper describes an acquisitive method of rule‐type knowledge from the field inspection data on highway bridges. The proposed method is enhanced by introducing an improvement to a traditional data mining technique, i.e. applying the rough set theory to the traditional decision table reduction method. The new rough set theory approach helps in cases of exceptional and contradictory data, which in the traditional decision table reduction method are simply removed from analyses. Instead of automatically removing all apparently contradictory data cases, the proposed method determines whether the data really is contradictory and therefore must be removed or not. The method has been tested with real data on bridge members including girders and filled joints in bridges owned and managed by a highway corporation in Japan. There are, however, numerous inconsistent data in field data. A new method is therefore proposed to solve the problem of data loss. The new method reveals some generally unrecognized decision rules in addition to generally accepted knowledge. Finally, a computer program is developed to perform calculation routines, and some field inspection data on highway bridges is used to show the applicability of the proposed method.

Rough Set Classification Algorithms Based on Texture Feature

2013 ◽  
Vol 347-350 ◽  
pp. 3119-3122
Author(s):  
Yan Xue Dong ◽  
Fu Hai Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Texture Classification ◽  
Texture Feature ◽  
Decision Table ◽  
Classification Algorithms ◽  
Reduction Algorithm ◽  
Discrete Method ◽  
Knowledge Reduction

The basic theory of rough set is given and a method for texture classification is proposed. According to the GCLM theory, texture feature is extracted and generate 32 feature vectors to form a decision table, find a minimum set of rules for classification after attribute discretization and knowledge reduction, experimental results show that using rough set theory in texture classification, accompanied by appropriate discrete method and reduction algorithm can get better classification results

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