A practical strategy for acquiring rules based on rough sets and principal component analysis

In order to obtain the attribute reducts and the concise rules with stronger generalization capabilities, we propose a practical strategy for acquiring rules based on rough set (RS) and principal component analysis (PCA), called here PSAR-RSPCA. In the PSARRSPCA, the collective correlation coefficie...

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Bibliographic Details
Published in2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583) Vol. 4; pp. 3146 - 3150 vol.4
Main Authors An Zeng, Qi-Lun Zheng, Pan, D., Hong Peng
Format Conference Proceeding
LanguageEnglish
Published Piscataway NJ IEEE 2004
Subjects
Annealing
Applied sciences
Benchmark testing
Computer science; control theory; systems
Control theory. Systems
Error analysis
Exact sciences and technology
Information systems
Knowledge acquisition
Mobile communication
Principal component analysis
Rough sets
Set theory
State-space methods
Correlation coefficient
Annealing
Heat treatment
Expert system
Error rate
Knowledge base
State space
State space method
Search algorithm
Decision table
Rough set theory
Genetic algorithm
Classification
Causality
Principal component analysis
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Summary:In order to obtain the attribute reducts and the concise rules with stronger generalization capabilities, we propose a practical strategy for acquiring rules based on rough set (RS) and principal component analysis (PCA), called here PSAR-RSPCA. In the PSARRSPCA, the collective correlation coefficient (CCC), as a quantitative index based on the essence of PCA, is used to measure the contribution of every condition attribute to "cause" (i.e. the state space constructed by the entire condition attributes), and RS is developed to keep "causality" (i.e. the dependencies between condition attributes and decision attributes) unchanged in a decision table. Meanwhile, PSAR-RSPCA absorbs the evolution ideas of gene algorithm and stimulated annealing algorithm to search for the attribute reduct with larger CCC. Compared with other algorithm, the test results show PSAR-RSPCA has an obvious reduction in the error rates of prediction (approximately 34.5%) by the well-known classification benchmark.
ISBN:0780385667
9780780385665
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2004.1400823