Precision parameter in the variable precision rough sets model: an application

Despite their diverse applications in many domains, the variable precision rough sets (VPRS) model lacks a feasible method to determine a precision parameter ( β ) value to control the choice of β -reducts. In this study we propose an effective method to find the β -reducts. First, we calculate a pr...

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Bibliographic Details
Published in:Omega (Oxford) 2006-04, Vol.34 (2), p.149-157
Main Authors: Su, Chao-Ton, Hsu, Jyh-Hwa
Format: Article
Language:English
Subjects:
[formula omitted]-reduct
Analysis
Applied sciences
Artificial intelligence
Comparative analysis
Computer science
control theory
systems
Connectionism. Neural networks
Data entry
Data mining
Exact sciences and technology
Information systems
Mathematical models
Mathematical programming
Neural networks
Operational research and scientific management
Operational research. Management science
Precision parameter
Studies
Technology application
VPRS model
VPRS model [beta]-reduct Precision parameter Neural networks
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Summary:Despite their diverse applications in many domains, the variable precision rough sets (VPRS) model lacks a feasible method to determine a precision parameter ( β ) value to control the choice of β -reducts. In this study we propose an effective method to find the β -reducts. First, we calculate a precision parameter value to find the subsets of information system that are based on the least upper bound of the data misclassification error. Next, we measure the quality of classification and remove redundant attributes from each subset. We use a simple example to explain this method and even a real-world example is analyzed. Comparing the implementation results from the proposed method with the neural network approach, our proposed method demonstrates a better performance.
ISSN:0305-0483
1873-5274
DOI:10.1016/j.omega.2004.08.005