Precision parameter in the variable precision rough sets model: an application
OmegaPublished 13 October 2004
Chao‐Ton Su, Jyh-Hwa Hsu
Citations80
SJR quartileQ1
SJR score2.31
SNIP2.20
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TL;DR
This study proposes an effective method to find the [beta]-reducts and compares the implementation results from the proposed method with the neural network approach, showing a better performance.
Abstract
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.
Keywords
Computer Science
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