Automatic Mining of Numerical Classification Rules with Parliamentary Optimization Algorithm
In recent years, classification rules mining has been one of the most important data mining tasks. In this study, one of the newest social-based metaheuristic methods, Parliamentary Optimization Algorithm (POA), is firstly used for automatically mining of comprehensible and accurate classification...
| Published in: | Advances in Electrical and Computer Engineering |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2015-11-01
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.4316/AECE.2015.04003 |
| Summary: | In recent years, classification rules mining has been one of the most important data mining tasks. In this study,
one of the newest social-based metaheuristic methods, Parliamentary Optimization Algorithm (POA), is firstly used
for automatically mining of comprehensible and accurate classification rules within datasets which have numerical
attributes. Four different numerical datasets have been selected from UCI data warehouse and classification rules
of high quality have been obtained. Furthermore, the results obtained from designed POA have been compared with the
results obtained from four different popular classification rules mining algorithms used in WEKA. Although POA is
very new and no applications in complex data mining problems have been performed, the results seem promising. The
used objective function is very flexible and many different objectives can easily be added to. The intervals of the
numerical attributes in the rules have been automatically found without any a priori process, as done in other
classification rules mining algorithms, which causes the modification of datasets. |
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| ISSN: | 1582-7445 1844-7600 |