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An association-based case reduction technique for case-based reasoning

Authors:

Cheng-Hsiang Liu,

Long-Sheng Chen,

Chun-Chin HsuAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 178, Issue 17

Pages 3347 - 3355

https://doi.org/10.1016/j.ins.2008.05.006

Published: 01 September 2008 Publication History

Abstract

Case-based reasoning (CBR) is a type of problem solving technique which uses previous cases to solve new, unseen and different problems. Although a larger number of cases in the memory can improve the coverage of the problem space, the retrieval efficiency will be downgraded if the size of the case-base grows to an unacceptable level. In CBR systems, the tradeoff between the number of cases stored in the case-base and the retrieval efficiency is a critical issue. This paper addresses the problem of case-base maintenance by developing a new technique, the association-based case reduction technique (ACRT), to reduce the size of the case-base in order to enhance the efficiency while maintaining or even improving the accuracy of the CBR. The experiments on 12 UCI datasets and an actual case from Taiwan's hospital have shown superior generalization accuracy for CBR with ACRT (CBR-ACRT) as well as a greater solving efficiency.

References

[1]

Agrawal, R. and Srikant, R., Fast algorithm for mining association rules. In: Proceeding of the 20th International Conference on Very Large Data Bases, Morgan Kaufmann, Santiago. pp. 487-499.]]

Digital Library

[2]

Aha, D.W., Kibler, D. and Albert, M.K., Instance-based learning algorithms. Machine Learning. v6. 37-66.]]

Digital Library

[3]

Brighton, H. and Mellish, C., Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery. v6. 153-172.]]

Digital Library

[4]

Bryant, S.M., A case-based reasoning approach to bankruptcy prediction modeling. International Journal of Intelligent Systems in Accounting, Finance and Management. v6 i3. 195-214.]]

[5]

Chang, C.-L., Finding prototypes for nearest neighbor classifiers. IEEE Transactions on Computers. v23-11. 1179-1184.]]

Digital Library

[6]

Chang, P.-C., Hsieh, J.-C. and Liao, T.W., A case-based reasoning approach for due-date assignment in a wafer fabrication factory. In: Proceedings of the 4th International Conference on Case-Based Reasoning, Springer-Verlag, Berlin. pp. 648-659.]]

Digital Library

[7]

Chen, G., Liu, H., Yu, L., Wei, Q. and Zhang, X., A new approach to classification based on association rule mining. Decision Support Systems. v42. 674-689.]]

Digital Library

[8]

Chiu, C.-C., Chang, P.-C. and Chiu, N.-H., A case-based expert support system for due-date assignment in a wafer fabrication factory. Journal of Intelligent Manufacturing. v14 i3-4. 287-296.]]

[9]

Finnie, G.R., Witting, G.E. and Desharnais, J., Estimating software development effort with case-based reasoning. In: Proceeding of the 2nd International Conference on Case-Based Reasoning, Springer Verlag, Berlin. pp. 13-22.]]

Digital Library

[10]

Gate, G.W., The reduced nearest neighbor rule. IEEE Transactions on Information Theory IT-18-3. 431-433.]]

Digital Library

[11]

Hart, P.E., The condensed nearest neighbor rule. IEEE Transactions on Information Theory. v14. 515-516.]]

Digital Library

[12]

Hsu, C.-C. and Ho, C.-S., A new hybrid case-based architecture for medical diagnosis. Information Sciences. v166 i1-4. 231-247.]]

Digital Library

[13]

Janssens, D., Wets, G., Brijs, T. and Vanhoof, K., Adapting the CBA algorithm by means of intensity of implication. Information Sciences. v173 i4. 305-318.]]

[14]

Jo, H., Han, I. and Lee, H., Bankruptcy prediction using case-based reasoning, neural networks and discriminant analysis. Expert Systems with Applications. v13 i2. 97-108.]]

[15]

Kim, K.S. and Han, I., The cluster-indexing method for case-based reasoning using self organising maps and learning vector quantisation for bond rating cases. Expert Systems with Applications. v21 i3. 147-156.]]

[16]

Lee, A.J.T., Hong, R.W., Ko, W.M., Tsao, W.K. and Lin, H.H., Mining spatial association rules in image databases. Information Sciences. v177 i7. 1593-1608.]]

Digital Library

[17]

Lee, G., Yang, W. and Lee, J.M., A parallel algorithm for mining multiple partial periodic patterns. Information Sciences. v176 i24. 3591-3609.]]

Digital Library

[18]

W. Li, J. Han, J. Pei, CMAR: accurate and efficient classification based on multiple class-association rules, in: ICDM 2001, San Jose: IEEE Computer Society, 2001, pp. 369-376.]]

Digital Library

[19]

Li, Y., Zhu, S., Wang, X.S. and Jajodia, S., Looking into the seeds of time: discovering temporal patterns in large transaction sets. Information Sciences. v176 i8. 1003-1031.]]

Digital Library

[20]

Liao, T.W., Zhang, Z.M. and Mount, C.R., A case-based reasoning system for identifying failure mechanisms. Engineering Applications of Artificial Intelligence. v13 i2. 199-213.]]

[21]

B. Liu, W. Hsu, Y. Ma, Integrating classification and association rule mining, in: KDD-98, 1998, New York.]]

[22]

Pan, R., Yang, Q. and Pan, S.J., Mining competent case bases for case-based reasoning. Artificial Intelligence. v171. 1039-1068.]]

Digital Library

[23]

Ritter, G.L., Woodruff, H.B., Lowry, S.R. and Isenhour, T.L., An algorithm for a selective nearest neighbor decision rule. IEEE Transactions on Information Theory. v21-6. 665-669.]]

Digital Library

[24]

Sha, D.Y. and Liu, C.-H., Development and evaluation of a tree-indexing approach to improve case-based reasoning: illustrated using the due date assignment problem. International Journal of Production Research. v44 i15. 3033-3049.]]

[25]

Shin, K.S. and Han, I., Case-based reasoning supported by genetic algorithms for corporate bond rating. Expert Systems with Applications. v16 i2. 85-95.]]

[26]

Shin, K.S. and Han, I., A case-based approach using inductive indexing for corporate bond rating. Decision Support Systems. v32 i1. 41-52.]]

Digital Library

[27]

Smyth, B. and Keane, M.T., Remembering to forget: a competence-preserving case deletion policy for case-based systems. In: Proceeding of the 13th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Francisco. pp. 377-382.]]

Digital Library

[28]

F. Thabtah, P. Cowling, Y. Peng, MCAR: multi-class classification based on association rule, in: The 3rd ACS/IEEE International Conference, 2005, pp. 33-39.]]

Digital Library

[29]

Tomek, I., An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics. v6 i6. 448-452.]]

[30]

Wang, C.Y., Tseng, S.S. and Hong, T.P., Flexible online association rule mining based on multidimensional pattern relations. Information Sciences. v176 i12. 1752-1780.]]

Digital Library

[31]

I. Watson, H. Watson, CAIRN: a case-based document retrieval system, in: N. Filer, I. Watson (Eds.), Proceedings of the 3rd United Kingdom Case-Based Reasoning Workshop, University of Manchester, 1997.]]

[32]

D. Wettschereck, A hybrid nearest-neighbor and nearest-hyperrectangle algorithm, in: F. Bergadano, L. de Raedt (Eds.), Proceedings of the 17th European Conference on Machine Learning, 1994, pp. 323-335.]]

Digital Library

[33]

Wilson, D.L., Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics. v2 i3. 408-421.]]

[34]

Willson, D.R. and Martinez, T.R., Reduction techniques for instance-based learning algorithms. Machine Learning. v38 i3. 257-286.]]

Digital Library

[35]

Yu, J.X., Chong, Z., Lu, H., Zhang, Z. and Zhou, A., A false negative approach to mining frequent itemsets from high speed transactional data streams. Information Sciences. v176 i14. 1986-2015.]]

Digital Library

Cited By

Fan ZLi YWang XLiu Y(2014)Hybrid similarity measure for case retrieval in CBR and its application to emergency response towards gas explosionExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.09.05141:5(2526-2534)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1016/j.eswa.2013.09.051
Rashedi ENezamabadi-Pour HSaryazdi S(2014)Long term learning in image retrieval systems using case based reasoningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2014.06.00935(26-37)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.engappai.2014.06.009
Li HSun J(2012)Case-based reasoning ensemble and business applicationExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.09.01739:3(3298-3310)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.09.017
Show More Cited By

Index Terms

An association-based case reduction technique for case-based reasoning
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
      1. Theorem proving algorithms

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 178, Issue 17

September, 2008

173 pages

ISSN:0020-0255

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Copyright © Elsevier Inc. © 2008.

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Elsevier Science Inc.

United States

Publication History

Published: 01 September 2008

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Cited By

Fan ZLi YWang XLiu Y(2014)Hybrid similarity measure for case retrieval in CBR and its application to emergency response towards gas explosionExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.09.05141:5(2526-2534)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1016/j.eswa.2013.09.051
Rashedi ENezamabadi-Pour HSaryazdi S(2014)Long term learning in image retrieval systems using case based reasoningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2014.06.00935(26-37)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.engappai.2014.06.009
Li HSun J(2012)Case-based reasoning ensemble and business applicationExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.09.01739:3(3298-3310)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.09.017
Lin SChen S(2011)Parameter tuning, feature selection and weight assignment of features for case-based reasoning by artificial immune systemApplied Soft Computing10.1016/j.asoc.2011.05.05411:8(5042-5052)Online publication date: 1-Dec-2011
https://dl.acm.org/doi/10.1016/j.asoc.2011.05.054
Wang SYeh S(2010)Framework of computer-assisted instruction and clinical decision support system for orthodontics with case-based reasoningProceedings of the Second international conference on Medical Biometrics10.1007/978-3-642-13923-9_37(344-352)Online publication date: 28-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13923-9_37
Cocea MMagoulas G(2009)Hybrid model for learner modelling and feedback prioritisation in exploratory learningInternational Journal of Hybrid Intelligent Systems10.5555/1735958.17359616:4(211-230)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.5555/1735958.1735961
Guan DYuan WLee YLee S(2009)Nearest neighbor editing aided by unlabeled dataInformation Sciences: an International Journal10.1016/j.ins.2009.02.011179:13(2273-2282)Online publication date: 1-Jun-2009
https://dl.acm.org/doi/10.1016/j.ins.2009.02.011
Lee JHa S(2009)Recognizing yield patterns through hybrid applications of machine learning techniquesInformation Sciences: an International Journal10.1016/j.ins.2008.11.008179:6(844-850)Online publication date: 1-Mar-2009
https://dl.acm.org/doi/10.1016/j.ins.2008.11.008

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