Abstract
The life cycle of Case-Based Reasoning (CBR) systems implies the maintenance of their knowledge containers for reasons of efficiency and competence. However, two main issues occur. First, knowledge within such systems is full of uncertainty and imprecision since they involve real-world experiences. Second, it is not obvious to choose from the wealth of maintenance policies, available in the literature, the most adequate one to preserve the competence towards problems’ solving. In fact, this competence is so difficult to be actually estimated due to the diversity of influencing factors within CBR systems. For that reasons, we propose, in this work, an entire evaluating process that allows to assess Case Base Maintenance (CBM) policies using information coming from both a statistical measure and a competence model under the belief function theory.
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Notes
- 1.
The factors identified with a star (\( ^{*} \)) are taken into account in the current work.
- 2.
The (k-NN) classifier is the most used within the CBR community.
- 3.
- 4.
Forthcoming research work will carry out with other evaluation criteria.
References
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. In: Artificial Intelligence Communications, pp. 39–52 (1994)
Leake, D.B., Wilson, D.C.: Categorizing case-base maintenance: dimensions and directions. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS, vol. 1488, pp. 196–207. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056333
Smyth, B., McKenna, E.: Modelling the competence of case-bases. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS, vol. 1488, pp. 208–220. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056334
Juarez, J.M., Craw, S., Lopez-Delgado, J.R., Campos, M.: Maintenance of case bases: current algorithms after fifty years. In: proceedings of the International Joint Conferences on Artificial Intelligence, pp. 5458–5463 (2018)
Smiti, A., Elouedi, Z.: Overview of maintenance for case based reasoning systems. Int. J. Comput. Appl. 32, 49–56 (2011)
Ben Ayed, S., Elouedi, Z., Lefevre, E.: ECTD: evidential clustering and case types detection for case base maintenance. In: Proceedings of the 14th International Conference on Computer Systems and Applications (AICCSA), pp. 1462–1469. IEEE (2017)
Ben Ayed, S., Elouedi, Z., Lefevre, E.: Exploiting domain-experts knowledge within an evidential process for case base maintenance. In: Destercke, S., Denoeux, T., Cuzzolin, F., Martin, A. (eds.) BELIEF 2018. LNCS (LNAI), vol. 11069, pp. 22–30. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99383-6_4
Smiti, A., Elouedi, Z.: SCBM: soft case base maintenance method based on competence model. Int. J. Comput. Sci. 25, 221–227 (2018)
Lupiani, E., Juarez, J.M., Palma, J.: Evaluating case-base maintenance algorithms. Knowl.-Based Syst. 67, 180–194 (2014)
Chebel-Morello, B., Haouchine, M.K., Zerhouni, N.: Case-based maintenance: structuring and incrementing the case base. Knowl.-Based Syst. 88, 165–183 (2015)
Hart, P.: The condensed nearest neighbor rule. IEEE Trans. Inf. Theory 14(3), 515–516 (1968)
Gates, G.: The reduced nearest neighbor rule. IEEE Trans. Inf. Theory 18(3), 431–433 (1972)
Ben Ayed, S., Elouedi, Z., Lefevre, E.: DETD: dynamic policy for case base maintenance based on EK-NNclus algorithm and case types detection. In: Medina, J., Ojeda-Aciego, M., Verdegay, J.L., Pelta, D.A., Cabrera, I.P., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2018. CCIS, vol. 853, pp. 370–382. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91473-2_32
Ben Ayed, S., Elouedi, Z., Lefèvre, E.: CEC-model: a new competence model for CBR systems based on the belief function theory. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 28–44. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_3
Mosqueira-Rey, E., Moret-Bonillo, V.: Validation of intelligent systems: a critical study and a tool. Expert Syst. Appl. 18(1), 1–16 (2000)
Smyth, B., Keane, M.T.: Remembering to forget: a competence-preserving deletion policy for CBR systems. In: The Thirteenth International Joint Conference on Artificial Intelligence, pp. 377–382 (1995)
Smiti, A., Elouedi, Z.: Modeling competence for case based reasoning systems using clustering. In: Proceedings of the 26th International FLAIRS Conference, the Florida Artificial Intelligence Research Society, pp. 399–404 (2013)
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38, 325–339 (1967)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Masson, M.H., Denœux, T.: ECM: an evidential version of the fuzzy c-means algorithm. Pattern Recognit. 41(4), 1384–1397 (2008)
Antoine, V., Quost, B., Masson, H.M., Denœux, T.: CECM: constrained evidential c-means algorithm. Comput. Stat. Data Anal. 56, 894–914 (2012)
Jousselme, A.L., Grenier, D., Bossé, E.: A new distance between two bodies of evidence. Inf. Fusion 2(2), 91–101 (2001)
Smets, P.: Application of the transferable belief model to diagnostic problems. Int. J. Intell. Syst. 13(2–3), 127–157 (1998)
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Ben Ayed, S., Elouedi, Z., Lefevre, E. (2019). Toward the Evaluation of Case Base Maintenance Policies Under the Belief Function Theory. In: Kern-Isberner, G., Ognjanović, Z. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2019. Lecture Notes in Computer Science(), vol 11726. Springer, Cham. https://doi.org/10.1007/978-3-030-29765-7_10
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