A Classification Knowledge Acquisition of Integrated Rough Sets Classifiers in the Banking Industry

2010 ◽  
Author(s):  
You-Shyang Chen ◽  
Ching-Hsue Cheng
Keyword(s):  
Knowledge Acquisition ◽  
Rough Sets ◽  
Banking Industry

Knowledge Acquisition from Rough Sets Using Merged Decision Rules

2018 ◽  
Vol 22 (3) ◽  
pp. 404-410
Author(s):  
Yoshiyuki Matsumoto ◽  
Junzo Watada ◽  
◽  
Keyword(s):  
Time Series ◽  
Knowledge Acquisition ◽  
Decision Rule ◽  
Rough Sets ◽  
Time Series Data ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Series Data ◽  
Economic Time Series ◽  
Knowledge Based

Rough set theory was proposed by Z. Pawlak in 1982. This theory can mine knowledge based on a decision rule from a database, a web base, a set, and so on. The decision rule is used for data analysis as well as calculating an unknown object. We analyzed time-series data using rough sets. Economic time-series data was predicted using decision rules. However, there are cases where an excessive number of decision rules exist, from which, it is difficult to acquire knowledge. In this paper, we propose a method to reduce the number of decision rules by merging them. Similar to how it is difficult to acquire knowledge from multiple rules, it is also difficult to acquire knowledge from rules with a large number of condition attributes. We propose a method to reduce the number of condition attributes and thereby reduce the number of rules. We analyze time-series data using this proposed method and acquire knowledge for prediction using decision rules. We use TOPIX and the yen–dollar exchange rate as knowledge-acquisition data. We propose a method to facilitate knowledge acquisition by merging rules.

An Attribute Reduction Algorithms of Expert System Knowledge Acquisition

2011 ◽  
Vol 48-49 ◽  
pp. 187-191 ◽  
Author(s):  
Yong Chang Ren ◽  
Tao Xing ◽  
Ping Zhu
Keyword(s):  
Expert System ◽  
Knowledge Acquisition ◽  
Rough Sets ◽  
Attribute Reduction ◽  
Attribute Importance ◽  
Software Cost ◽  
Rough Sets Theory ◽  
And Performance ◽  
Sets Theory ◽  
Method Construction

Knowledge acquisition is the bottleneck of construction expert system, to provide an accurate inference of knowledge is the key decision-making plan. This article use the rough sets theory, through the rough sets reduction eliminate redundant condition attribute, to achieve the streamlining of the knowledge library. First study the knowledge acquisition, in exposition knowledge hierarchical structure foundation, has given the conceptualization, formal, the knowledge library refinement and so on three knowledge acquisition; and then study attributes reduction algorithms, in the research sets difference and the attribute importance, the reduction algorithms inferential reasoning process's foundation, has given the attribute reduction algorithms six steps. Finally, according to the attributes reduction algorithms and the steps, to estimate the expert system to the function analytic method construction software cost, the composition technology complexity factor of 14 factors reduction. The results showed that the use of rough sets theory to reduce the attributes, can simplify the structure of complex systems, and can effectively maintain the knowledge library structure and performance.

Data Processing in Attributes Reduction Based on Rough Sets and FCA

2014 ◽  
Vol 1014 ◽  
pp. 480-483
Author(s):  
Zhi Hao Peng ◽  
Wei Luo ◽  
An Sheng Deng
Keyword(s):  
Data Processing ◽  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Formal Context ◽  
Challenging Problem ◽  
Future Perspectives ◽  
Knowledge Reduction

Knowledge reduction is one of the basic contents in rough set theory and the most challenging problem in knowledge acquisition. In this paper, an algorithm is proposed, which aims to get all the reducts based on the attributes of the formal context. Experiments show that the algorithm is sound and accurate. Finally, further work and future perspectives are discussed.

Sign in / Sign up

Export Citation Format

Share Document