Abstract
Instance-based methods have been successfully applied to numerical prediction (regression) tasks in many domains. Such methods often rely on a simple combination function to generate a prediction from past instances. Case-based reasoning for regression adds a richer case adaptation step to adjust prior solutions to fit new problems. This article presents a new approach to case adaptation for case-based regression systems, based on applying an ensemble of case adaptation rules generated automatically from pairs of cases in the case base, using the case difference heuristic. It evaluates the method’s performance, considering in particular the effects of using local versus global case information to generate adaptation rules from the case base. Experimental results support that the proposed method generally outperforms baselines and that the accuracy of adaptation based on locally-generated rules is highly competitive with that of global rule-generation methods considering many more cases.
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References
Aha, D., Kibler, D., & Albert, M. (1991). Instance-based learning algorithms. Machine Learning, 6(1), 37–66.
Arshadi, N., & Jurisica, I. (2005). An ensemble of case-based classifiers for high-dimensional biological domains. In Proceedings of the sixth international conference on case-based resoning. ICCBR’05 (pp. 21–34). Berlin: Springer Verlag.
Bay, S.D. (1998). Combining nearest neighbor classifiers through multiple feature subsets. In Proc. 15th international conf. on machine learning (pp. 37–45).
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
Breiman, L. (2001). Random forests. Machinge Learning, 45(1), 5–32.
Craw, S., Jarmulak, J., & Rowe, R. (2001). Learning and applying case-based adaptation knowledge. In Aha, D., & Watson, I. (Eds.) Proceedings of the fourth international conference on case-based reasoning (pp. 131–145). Berlin: Springer Verlag.
Cunningham, P., & Zenobi, G. (2001). Case representation issues for case-based reasoning from ensemble research. In Case-based reasoning research and development: proceedings of the fourth international conference on case-based reasoning. Berlin: Springer Verlag.
Dietterich, T. (2000). Ensemble methods in machine learning. In Proceedings of the first international workshop on multiple classifier systems (pp. 1–15). Berlin: Springer Verlag.
Frank, A., & Asuncion, A. (2010). UCI machine learning repository. http://archive.ics.uci.edu/ml.
Fuchs, B., Lieber, J., Mille, A., & Napoli, A. (2014). Differential adaptation: an operational approach to adaptation for solving numerical problems with CBR. Knowledge-Based Systems, 68, 103–114. Enhancing Experience Reuse and Learning.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I.H. (2009). The WEKA data mining software: an update. SIGKDD Explorer Newsletter, 11(1), 10–18.
Hanney, K., & Keane, M. (1997). The adaptation knowledge bottleneck: How to ease it by learning from cases. In Proceedings of the Second International Conference on Case-Based Reasoning (pp. 359–370). Berlin: Springer Verlag.
Jalali, V., & Leake, D. (2013a). A context-aware approach to selecting adaptations for case-based reasoning. In Modeling and using context (pp. 101–114). Berlin: Springer.
Jalali, V., & Leake, D. (2013b). Extending case adaptation with automatically-generated ensembles of adaptation rules. In Case-based reasoning research and development, ICCBR, (Vol. 2013 pp. 188–202). Berlin: Springer.
Jalali, V., & Leake, D. (2013c). On deriving adaptation rule confidence from the rule generation process. In Case-based reasoning research and development, ICCBR, (Vol. 2013 pp. 179–187). Berlin: Springer-Verlag.
Jalali, V., & Leake, D. (2014a). Adaptation-guided case base maintenance. In Proceedings of the twenty-eighth conference on artificial intelligence (pp. 1875–1881). AAAI Press.
Jalali, V., & Leake, D. (2014b). An ensemble approach to adaptation-guided retrieval. In Proceedings of the 2014 Florida AI research symposium (pp. 295–300). AAAI Press.
Jalali, V., & Leake, D. (2014c). On retention of adaptation rules. In Case-based reasoning research and development, ICCBR (p. 2014). Berlin: Springer.
Jarmulak, J., Craw, S., & Rowe, R. (2001). Using case-base data c for design. In Proceedings of the 17th international joint conference on artificial intelligence - Volume 2. IJCAI’01 (pp. 1011–1016). San Francisco: Morgan Kaufmann Publishers Inc.
Kibler, D., Aha, D.W., & Albert, M.K. (1989). Instance-based prediction of real-valued attributes. Computational Intelligence, 5(2), 51–57.
Kolodner, J., & Leake, D. (1996). A tutorial introduction to case-based reasoning. In Leake, D. (Ed.) Case-based reasoning: experiences, lessons, and future directions (pp. 31–65). Menlo Park: AAAI Press.
Li, H., & Sun, J. (2012). Case-based reasoning ensemble and business application: a computational approach from multiple case representations driven by randomness. Expert Systems with Applications, 39(3), 3298–3310.
López de Mántaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M., Cox, M., Forbus, K., Keane, M., Aamodt, A., & Watson, I. (2005). Retrieval, reuse, revision, and retention in CBR. Knowledge Engineering Review, 20(3).
McDonnell, N., & Cunningham, P. (2006). A knowledge-light approach to regression using case-based reasoning. In Proceedings of the 8th European conference on case-based reasoning. ECCBR’06 (pp. 91–105). Berlin: Springer-Verlag.
McSherry, D. (1998). An adaptation heuristic for case-based estimation. In Proceedings of the fourth European workshop on advances in case-based reasoning. EWCBR ’98 (pp. 184–195). London: Springer-Verlag.
Patterson, D., Rooney, N., & Galushka, M. (2002). A regression based adaptation strategy for case-based reasoning. In Proceedings of the eighteenth annual national conference on artificial intelligence (pp. 87–92): AAAI Press.
Policastro, C.A., Carvalho, A.C., & Delbem, A.C. (2008). A hybrid case adaptation approach for case-based reasoning. Applied Intelligence, 28(2), 101–119.
Schapire, R.E. (1990). The strength of weak learnability. Machine Learning, 5 (2), 197–227.
Smyth, B., & Keane, M. (1998). Adaptation-guided retrieval: questioning the similarity assumption in reasoning. Artificial Intelligence, 102(2), 249–293.
Wilke, W., Vollrath, I., Althoff, K.D., & Bergmann, R. (1997). A framework for learning adaptation knowledge based on knowledge light approaches. In Proceedings of the fifth german workshop on case-based reasoning (pp. 235–242).
Wiratunga, N., Craw, S., & Rowe, R. (2002). Learning to adapt for case-based design. In Proceedings of the 6th European conference on advances in case-based reasoning. ECCBR ’02 (pp. 421–435). London: Springer-Verlag.
Wiratunga, N., Craw, S., & Rowe, R. (2006). Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence, 170, 1175–1192.
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This article is adapted and extended from Jalali and Leake (2013b).
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Jalali, V., Leake, D. Enhancing case-based regression with automatically-generated ensembles of adaptations. J Intell Inf Syst 46, 237–258 (2016). https://doi.org/10.1007/s10844-015-0377-0
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DOI: https://doi.org/10.1007/s10844-015-0377-0