SCBM: soft case base maintenance method based on competence model

Abstract This paper concerns one of intelligent computational techniques which is case based reasoning (CBR) more particularly the case base maintenance (CBM). It aims to ensure the CBR systems quality. Throughout this paper, we were faced to a problematic question: how to shrink the size of the case base while preserving as much as possible the performance and the competence of the CBR system in soft context. To answer this question, we have first analyzed and revised the theoretical foundations of the existing CBM methods. Then, we have proposed a novel soft case base maintenance (SCBM) method based on a soft competence model (SCM) and a fuzzy clustering technique. Our method has the objective to guarantee the CBR systems efficiency in terms of improving the competence, and reducing both the storage requirements and search time. We support our approach with empirical evaluation using different benchmark data sets to show the effectiveness of our method in terms of improving the competence and the performance of the system.

[1]  Zied Elouedi,et al.  Competence and Performance-Improving approach for maintaining Case-Based Reasoning Systems , 2012 .

[2]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[3]  Javier Bajo,et al.  Hybrid Neural Intelligent System to Predict Business Failure in Small-to-Medium-Size Enterprises , 2011, Int. J. Neural Syst..

[4]  Michael T. Manry,et al.  Prototype Classifier Design with Pruning , 2005, Int. J. Artif. Intell. Tools.

[5]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[6]  Zied Elouedi,et al.  WCOID: Maintaining case-based reasoning systems using Weighting, Clustering, Outliers and Internal cases Detection , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[7]  Jing Wu,et al.  Keep It Simple: A Case-Base Maintenance Policy Based on Clustering and Information Theory , 2000, Canadian Conference on AI.

[8]  Thomas Roth-Berghofer,et al.  On Quality Measures for Case Base Maintenance , 2000, EWCBR.

[9]  David C. Wilson,et al.  Maintaining Case‐Based Reasoners: Dimensions and Directions , 2001, Comput. Intell..

[10]  David Leake,et al.  Case-Based Reasoning: Experiences, Lessons and Future Directions , 1996 .

[11]  Habib Chabchoub,et al.  A case based reasoning based multi-agent system for the reactive container stacking in seaport terminals , 2017, ICCS.

[12]  Dianhui Wang,et al.  Case-based reasoning classifier based on learning pseudo metric retrieval , 2017, Expert Syst. Appl..

[13]  Zied Elouedi,et al.  Soft DBSCAN: Improving DBSCAN clustering method using fuzzy set theory , 2013, 2013 6th International Conference on Human System Interactions (HSI).

[14]  Chien-Hsing Chou,et al.  The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[15]  Zied Elouedi,et al.  Modeling Competence for Case Based Reasoning Systems Using Clustering , 2013, FLAIRS Conference.

[16]  Zied Elouedi,et al.  COID: Maintaining Case Method Based on Clustering, Outliers and Internal Detection , 2010 .

[17]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.