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Performance of case-based reasoning retrieval using classification based on associations versus Jcolibri and FreeCBR : a further validation study

Aljuboori, AS; Coenen, F; Nsaif, M; Parsons, DJ

Performance of case-based reasoning retrieval using classification based on associations versus Jcolibri and FreeCBR : a further validation study Thumbnail


Authors

AS Aljuboori

F Coenen

M Nsaif

DJ Parsons



Abstract

Case-Based Reasoning (CBR) plays a major role in expert system research. However, a critical problem can be met when a CBR system retrieves incorrect cases. Class Association Rules (CARs) have been utilized to offer a potential solution in a previous work. The aim of this paper was to perform further validation of Case-Based Reasoning using a Classification based on Association Rules (CBRAR) to enhance the performance of Similarity Based Retrieval (SBR). The CBRAR strategy uses a classed frequent pattern tree algorithm (FP-CAR) in order to disambiguate wrongly retrieved cases in CBR. The research reported in this paper makes contributions to both fields of CBR and Association Rules Mining (ARM) in that full target cases can be extracted from the FP-CAR algorithm without invoking P-trees and union operations. The dataset used in this paper provided more efficient results when the SBR retrieves unrelated answers. The accuracy of the proposed CBRAR system outperforms the results obtained by existing CBR tools such as Jcolibri and FreeCBR.

Citation

Aljuboori, A., Coenen, F., Nsaif, M., & Parsons, D. (2018). Performance of case-based reasoning retrieval using classification based on associations versus Jcolibri and FreeCBR : a further validation study. Journal of Physics: Conference Series, 1003(1), https://doi.org/10.1088/1742-6596/1003/1/012130

Journal Article Type Article
Acceptance Date Apr 4, 2018
Online Publication Date May 25, 2018
Publication Date May 25, 2018
Deposit Date Jul 26, 2018
Publicly Available Date Jul 26, 2018
Journal Journal of Physics: Conference Series
Print ISSN 1742-6588
Electronic ISSN 1742-6596
Publisher IOP Publishing
Volume 1003
Issue 1
DOI https://doi.org/10.1088/1742-6596/1003/1/012130
Publisher URL https://doi.org/10.1088/1742-6596/1003/1/012130
Related Public URLs http://iopscience.iop.org/journal/1742-6596
Additional Information Additional Information : Ibn Al-Haitham First International Scientific Conference, 13-14 December 2017, Baghdad, Iraq

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