# Case-base maintenance with multi-objective evolutionary algorithms

- 292 Downloads
- 2 Citations

## Abstract

Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solving ability of the Case-Base Reasoning system. However, when the case-base has many cases, then performance problems arise due to the time needed to find those similar cases to the input problem. At this point, Case-Base Maintenance algorithms can be used to reduce the number of cases and maintain the accuracy of the Case-Base Reasoning system at the same time. Whereas Case-Base Maintenance algorithms typically use a particular heuristic to remove (or select) cases from the case-base, the resulting maintained case-base relies on the proportion of redundant and noisy cases that are present in the case-base, among other factors. That is, a particular Case-Base Maintenance algorithm is suitable for certain types of case-bases that share some indicators, such as redundancy and noise levels. In the present work, we consider Case-Base Maintenance as a multi-objective optimization problem, which is solved with a Multi-Objective Evolutionary Algorithm. To this end, a fitness function is introduced to measure three different objectives based on the Complexity Profile model. Our hypothesis is that the Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance may be used in a wider set of case-bases, achieving a good balance between the reduction of cases and the problem-solving ability of the Case-Based Reasoning system. Finally, from a set of the experiments, our proposed Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance shows regularly good results with different sets of case-bases with different proportion of redundant and noisy cases.

## Keywords

Case-based reasoning Case-base maintenance Multi-objective evolutionary algorithms## Notes

### Acknowledgments

This work was partially funded by the Seneca Research Foundation of the Region of Murcia under project 15277/PI/10, and by the Spanish Ministry of Science and Innovation+European FEDER+PlanE funds under the project TIN2009-14372-C03-01.

## References

- Aamodt, A., & Plaza, E. (1994). Case-based reasoning: foundational issues, methodological variations, and system approaches.
*AI Communications*,*7*, 39–59.Google Scholar - Aha, D. (1992). Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms.
*International Journal Of Man-Machine Studies*,*36*(2), 267–287.CrossRefGoogle Scholar - Aha, D., Kibler, D., & Albert, M. (1991). Instance-based learning algorithms.
*Machine Learning*,*6*(1), 37–66.Google Scholar - Ahn, H., Kim, K.j., & Han, I. (2007). A case-based reasoning system with the two-dimensional reduction technique for customer classification.
*Expert Systems with Applications*,*32*(4), 1011–1019.CrossRefGoogle Scholar - Brighton, H., & Mellish, C. (1999). On the consistency of information filters for lazy learning algorithms. In
*Principles of data mining and knowledge discovery, lecture notes in artificial intelligence*(Vol. 1704, pp. 283–288).Google Scholar - Bunke, H., Irniger, C., & Neuhaus, M. (2005). Graph matching - challenges and potential solutions. In
*Proceedings of the 13th international conference on image analysis and processing, ICIAP’05*(pp. 1–10).Google Scholar - Coello, C.C., Lamont, G., & van Veldhuizen, D. (2007). Evolutionary algorithms for solving multi-objective problems.
*Genetic and Evolutionary Computation*.Google Scholar - Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification.
*IEEE Transactions on Information Theory*,*13*(1), 21–27.CrossRefzbMATHGoogle Scholar - Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II.
*IEEE Transactions on Evolutionary Computation*,*6*(2), 182–197.CrossRefGoogle Scholar - Eiben, A.E., & Smith, J.E. (2003).
*Introduction to Evolutionary Computing*. SpringerVerlag.Google Scholar - Francis, A., & Ram, J.A. (1993). Computational models of the utility problem and their application to a utility analysis of case-based reasoning. In
*Proceedings of the workshop on knowledge compilation and speed-up learning*(pp. 48–55).Google Scholar - Frank, A., & Asuncion, A. (2010). UCI machine learning repository. http://archive.ics.uci.edu/ml.
- Gates, G. (1972). Reduced nearest neighbor rule.
*IEEE Transactions on Information Theory*,*18*(3), 431+.CrossRefGoogle Scholar - Gil, Y. (2012). Reproducibility and efficiency of scientific data analysis: scientific workflows and case-based reasoning. In
*Case-based reasoning research and development, lecture notes in computer Science*(Vol. 7466, pp. 2–2).Google Scholar - Göker, M.H., & Roth-Berghofer, T. (1999). The development and utilization of the case-based help-desk support system {HOMER}.
*Engineering Applications of Artificial Intelligence*,*12*(6), 665–680.CrossRefGoogle Scholar - Grefenstette, J. (1986). Optimization of control parameters for genetic algorithms.
*IEEE Transactions on Systems, Man, and Cybernetics*,*16*(1), 122–128.CrossRefGoogle Scholar - Hart, P. (1968). Condensed nearest neighbor rule.
*IEEE Transactions on Information Theory*,*14*(3), 515+.CrossRefGoogle Scholar - Holland, J.H. (1975).
*Adaptation in natural and artificial systems*. MIT Press.Google Scholar - Ishibuchi, H., Nakashima, T., & Nii, M. (2001). Genetic-algorithm-based instance and feature selection. In
*Instance selection and construction for data mining*(Vol. 608, pp. 95–112).Google Scholar - Jong, K.A.D., & Spears, W.M. (1990). An analysis of the interacting roles of population size and crossover in genetic algorithms. In
*PPSN*(pp. 38–47).Google Scholar - Juarez, J.M., Guil, F., Palma, J., & Marin, R. (2009). Temporal similarity by measuring possibilistic uncertainty in CBR.
*Fuzzy Sets And Systems*,*160*(2), 214–230.MathSciNetCrossRefzbMATHGoogle Scholar - Kibler, D., & Aha, D. (1987). Learning representative exemplars of concepts: an initial case study. In
*Proceedings of the fourth international workshop on machine learning*(pp. 24–30).Google Scholar - Kim, K., & Han, I. (2001). Maintaining case-based reasoning systems using a genetic algorithms approach.
*Expert Systems With Applications*,*21*(3), 139–145.CrossRefGoogle Scholar - Laumanns, M., Zitzler, E., & Thiele, L. (2001). On the effects of archiving, elitism, and density based selection in evolutionary multi-objective optimization. In
*EMO*(pp. 181–196).Google Scholar - Leake, D., & Wilson, D. (1998). Categorizing case-base maintenance: dimensions and directions. In
*Advances in case-based reasoning, LNAI*(Vol. 1488, pp. 196–207).Google Scholar - Leake, D., & Wilson, M. (2011). How many cases do you need? assessing and predicting case-base coverage. In
*19th international conference on case-based reasoning research and development, ICCBR’11*(pp. 92–106).Google Scholar - Lopez de Mantaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M.L., Cox, M.T., Forbus, K., Keane, M., Aamodt, A., & Watson, I. (2005). Retrieval, reuse, revision and retention in case-based reasoning.
*The Knowledge Engineering Review*,*20*, 215–240.CrossRefGoogle Scholar - Markovitch, S., & Scott, P. (1988). The role of forgetting in learning. In
*Proceedings of the fifth international conference on machine learning*(pp. 459–465).Google Scholar - Massie, S., Craw, S., & Wiratunga, N. (2005). Complexity-guided case discovery for case based reasoning. In
*20th national conference on artificial intelligence - volume 1, AAAI’05*(pp. 216–221).Google Scholar - Massie, S., Craw, S., & Wiratunga, N. (2006). Complexity profiling for informed case-base editing. In
*Advances in case-based reasoning, LNAI*(Vol. 4106, pp. 325–339).Google Scholar - Montani, S., Portinale, L., Leonardi, G., Bellazzi, R., & Bellazzi, R. (2006). Case-based retrieval to support the treatment of end stage renal failure patients.
*Artificial Intelligence in Medicine*,*37*(1), 31–42.CrossRefGoogle Scholar - Olsson, E., Funk, P., & Xiong, N. (2004). Fault diagnosis in industry using sensor readings and case-based reasoning.
*Journal of Intelligent and Fuzzy Systems*,*15*(1), 41–46.Google Scholar - Pan, R., Yang, Q., & Pan, S. (2007). Mining competent case bases for case-based reasoning.
*Artificial Intelligence*,*171*(16–17), 1039–1068.MathSciNetCrossRefzbMATHGoogle Scholar - Riesbeck, R.S.C. (1989).
*Inside case-based reasoning*. Lawrence Erlbaum.Google Scholar - Rissland, E.L. (2009). Black swans, gray cygnets and other rare birds. In
*Proceedings of the 8th international conference on case-based reasoning: case-based reasoning research and development, ICCBR ’09*(pp. 6–13).Google Scholar - Smyth, B., & Keane, M. (1995). Remembering to forget - a competence-preserving case deletion policy for case-based reasoning systems. In
*IJCAI’95, international joint conference on artificial intelligence*(pp. 377–382).Google Scholar - Smyth, B., & Mckenna, E. (1999). Building compact competent case-bases. In
*Case-based reasoning research and development, lecture notes in artificial intelligence*(Vol. 1650, pp. 329–342).Google Scholar - Tomek, I. (1976). Experiment with edited nearest-neighbor rule.
*IEEE Transactions On Systems Man And Cybernetics*,*6*(6), 448–452.MathSciNetCrossRefzbMATHGoogle Scholar - Watson, I. (1998). Is cbr a technology or a methodology? In
*Tasks and methods in applied artificial intelligence, LNCS*(Vol. 1416, pp. 525–534).Google Scholar - Wilson, D. (1972). Asymptotic properties of nearest neighbor rules using edited data.
*IEEE Transactions on Systems Man and Cybernetics*,*SMC2*(3), 408–&.MathSciNetCrossRefzbMATHGoogle Scholar - Wilson, D., & Martinez, T. (2000). Reduction techniques for instance-based learning algorithms.
*Machine Learning*,*38*(3), 257–286.CrossRefzbMATHGoogle Scholar - Wilson, D.R., & Martinez, T.R. (1997). Instance pruning techniques. In
*Machine learning: proceedings of the fourteenth international conference (ICML97)*(pp. 404–411). Morgan Kaufmann.Google Scholar - Yu, X., & Gen, M. (2010).
*Introduction to evolutionary algorithms, decision engineering.*Springer.Google Scholar - Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach.
*IEEE Transactions on Evolutionary Computation*,*3*(4), 257–271.CrossRefGoogle Scholar