Orders > Conference proceedings > Software, knowledge, information management and applications (SKIMA 2013)
Proceedings of the International Conference on
Software, Knowledge, Information Management and Applications (SKIMA 2013)
Advanced Technology Solutions and Applications in Higher Education and Enterprises |
(from Chapter 6: Classification Algorithms and Applications)
| Full Citation and Abstract
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Title: |
A prediction model based on multiple support and associative classification approaches |
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Author(s): |
Bilal Sowan, Keshav Dahal, Alamgir Hossain |
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Address: |
Faculty of Information Technology, Applied Science University, Amman, Jordan
School of Engineering and Computing, University of the West of Scotland, High Street, Paisley, PA1 2BE, UK
Faculty of Science and Technology, Anglia Ruskin University, UK
b_sowan @ asu.edu.jo, keshav.dahal @ uws.ac.uk, alamgir.hossain @ anglia.ac.uk |
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Reference: |
SKIMA 2013 Proceedings pp. 292 - 302 |
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Abstract/
Summary |
In this paper, a fuzzy classification association rule (FCAR) model is proposed based on the improved G-K algorithm, improved multiple supports and the proposed fuzzy associative classification rules (FACR) approaches to improve the prediction accuracy. The improved G-K algorithm is used to define the membership functions of fuzzy sets, while FACR improves current associative classification approaches by adapting the improved multiple support algorithm. The proposed FCAR model can provide a generalized prediction model to deal with different application domains. The validation of the FCAR model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and knowledge extraction based on evolutionary learning (KEEL) repositories, then the results of FCAR are compared with common prediction model artificial neural network (ANN). The experimental results show that the proposed model discovers rules that effectively minimize the prediction error rate. |
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