An extended Chi2 algorithm for discretization of real value attributes

The variable precision rough sets (VPRS) model is a powerful tool for data mining, as it has been widely applied to acquire knowledge. Despite its diverse applications in many domains, the VPRS model unfortunately cannot be applied to real-world classification tasks involving continuous attributes....

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2005-03, Vol.17 (3), p.437-441
Main Authors: SU, Chao-Ton, HSU, Jyh-Hwa
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Approximation
Approximation algorithms
Chaos
Classification
Classification algorithms
Computer science
control theory
systems
Data mining
Discretization
Entropy
Error correction
Exact sciences and technology
Index Terms- VPRS model
Information systems. Data bases
Mathematical analysis
Memory organisation. Data processing
Rough sets
RST
Running
Software
Software algorithms
Software performance
Studies
Tasks
Uncertainty
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Summary:The variable precision rough sets (VPRS) model is a powerful tool for data mining, as it has been widely applied to acquire knowledge. Despite its diverse applications in many domains, the VPRS model unfortunately cannot be applied to real-world classification tasks involving continuous attributes. This requires a discretization method to preprocess the data. Discretization is an effective technique to deal with continuous attributes for data mining, especially for the classification problem. The modified Chi2 algorithm is one of the modifications to the Chi2 algorithm, replacing the inconsistency check in the Chi2 algorithm by using the quality of approximation, coined from the rough sets theory (RST), in which it takes into account the effect of degrees of freedom. However, the classification with a controlled degree of uncertainty, or a misclassification error, is outside the realm of RST. This algorithm also ignores the effect of variance in the two merged intervals. In this study, we propose a new algorithm, named the extended Chi2 algorithm, to overcome these two drawbacks. By running the software of See5, our proposed algorithm possesses a better performance than the original and modified Chi2 algorithms.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2005.39