×

zbMATH — the first resource for mathematics

Dynamic maintenance case base using knowledge discovery techniques for case based reasoning systems. (English) Zbl 1432.68400
Summary: The achievement of a Case Based Reasoning (CBR) system is strongly related to the quality of case data and the rapidity of the retrieval process that depends on the quantity of the cases. This quality can diminish especially when the number of cases gets outsized. To guarantee this quality, maintenance the case base becomes essentially. Much existing maintenance CBR approaches focus on the performance of the CBR or the study of the case base (CB) competence. Even though the two points are directly related, there is a few research on using strategies at both points at the same time. Furthermore, the proposed methods are not dynamic, they are not suitable for the frequently change in learning process. In this paper, we propose maintenance CBR method based on well-organized machine learning techniques, in the process of improving the competence and the performance of the CB and can handle incremental cases which evolve over time. We support our approach with empirical evaluation using different benchmark data sets to show the effectiveness of our method.
MSC:
68T05 Learning and adaptive systems in artificial intelligence
Software:
UCI-ml
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Aamodt, A.; Plaza, E., Case-based reasoning: foundational issues, methodological variations, and system approaches, AI Commun., 7, 1, 39-52 (1994)
[2] Kolodner, J., An introduction to case-based reasoning, Artif. Intell. Rev., 6, 1, 3-34 (1992)
[3] Borrajo, a. B.B. M.; Corchado, E.; Bajo, J.; Corchado, J. M., Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises, Int. J. Neural Syst., 21, 4, 277-296 (2011)
[4] Abraham, A., Hybrid approaches for approximate reasoning, J. Intell. Fuzzy Syst., 23, 2-3, 41-42 (2012)
[5] Anthony, M.; Ratsaby, J., A probabilistic approach to case-based inference, Theor. Comput. Sci., 589, 61-75 (2015), URL · Zbl 1317.68141
[6] Khosravani, M. R.; Nasiri, S.; Weinberg, K., Application of case-based reasoning in a fault detection system on production of drippers, Appl. Soft Comput., 75, 227-232 (2019), URL
[7] Smiti, A.; Elouedi, Z., WCOID: maintaining case-based reasoning systems using Weighting, Clustering, Outliers and Internal cases Detection, (Proceedings of the Eleventh International on Intelligent Systems Design and Applications. Proceedings of the Eleventh International on Intelligent Systems Design and Applications, ISDA 2011 (2011)), 37-42
[8] Asuncion, A.; Newman, D., UCI machine learning repository (2007), URL
[9] Smiti, A.; Elouedi, Z., Article: overview of maintenance for case based reasoning systems, Int. J. Comput. Appl., 32, 2, 49-56 (2011), published by Foundation of Computer Science, New York
[10] Markovitch, S.; Scott, P. D., The role of forgetting in learning, (Proceedings of the Fifth International Conference on Machine Learning (1988), Morgan Kaufmann), 459-465
[11] Minton, S., Qualitative results concerning the utility of explanation-based learning, Artif. Intell., 42, 363-391 (1990)
[12] Smiti, A.; Elouedi, Z., Using clustering for maintaining case based reasoning systems, (Proceedings of the 5th International Conference on Modeling, Simulation and Applied Optimization. Proceedings of the 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO’2013 (2013), IEEE), 1-6
[13] Smiti, A.; Elouedi Coid, Z., Maintaining case method based on clustering, outliers and internal detection, (Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010, SNPD’10, vol. 295 (2010), Springer: Springer Berlin/Heidelberg), 39-52
[14] Cao, G.; Shiu, S.; Wang, X., A fuzzy-rough approach for case base maintenance, (Proceedings of the International Conference on Case Based Reasoning (2001)), 118-130 · Zbl 0982.68509
[15] Khosravani, M. R.; Nasiri, S.; Anders, D.; Weinberg, K., Prediction of dynamic properties of ultra-high performance concrete by an artificial intelligence approach, Adv. Eng. Softw., 127, 51-58 (2019), URL
[16] Salamó, M.; Golobardes, E., Hybrid deletion policies for case base maintenance, (Proceedings of the Florida Artificial Intelligence Research Society FLAIRS-2003 (2003), AAAI Press), 150-154
[17] Smyth, B., Case-base maintenance, (Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE. Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE, London, UK (1998)), 507-516
[18] Chou, C. H.; Kuo, B. H.; Chang, F., The generalized condensed nearest neighbor rule as a data reduction method, (Proceedings of the International Conference on Pattern Recognition, vol. 2 (2006)), 556-559
[19] Manry, J.; Yu, M. T.; Wilson, D. R., Prototype classifier design with pruning, Int. J. Artif. Intell. Tools, 261-280 (2005)
[20] Wilson, D. L., Asymptotic properties of nearest neighbor rules using edited data, IEEE Trans. Syst. Man Cybern., 2, 3, 408-421 (1972) · Zbl 0276.62060
[21] Aha, D. W.; Kibler, D.; Albert, M. K., Instance-based learning algorithms, (Machine Learning (1991)), 37-66
[22] Smiti, A.; Elouedi, Z., Modeling competence for case based reasoning systems using clustering, (The 26th International FLAIRS Conference (2013), The Florida Artificial Intelligence Research Society: The Florida Artificial Intelligence Research Society Florida, USA), 399-404
[23] Smiti, A.; Elouedi, Z., Dynamic DBSCAN-GM clustering algorithm, (The 16th IEEE International Symposium on Computational Intelligence and Informatics. The 16th IEEE International Symposium on Computational Intelligence and Informatics, CNTI (2015), IEEE Computer Society: IEEE Computer Society Budapest), 311-316
[24] Smiti, A.; Elouedi, Z., DBSCAN-GM: an improved clustering method based on Gaussian means and DBSCAN techniques, (Proceedings of the International Conference on Intelligent Engineering Systems. Proceedings of the International Conference on Intelligent Engineering Systems, INES (2012), IEEE Computer Society), 573-578
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.