Methods for Case Maintenance in Case-Based Reasoning
Case-Based Reasoning (CBR) is one of machine
learning algorithms for problem solving and learning that caught a lot
of attention over the last few years. In general, CBR is composed of
four main phases: retrieve the most similar case or cases, reuse the
case to solve the problem, revise or adapt the proposed solution, and
retain the learned cases before returning them to the case base for
learning purpose. Unfortunately, in many cases, this retain process
causes the uncontrolled case base growth. The problem affects
competence and performance of CBR systems. This paper proposes
competence-based maintenance method based on deletion policy
strategy for CBR. There are three main steps in this method. Step 1,
formulate problems. Step 2, determine coverage and reachability set
based on coverage value. Step 3, reduce case base size. The results
obtained show that this proposed method performs better than the
existing methods currently discussed in literature.
[1] D.W. Aha, "Feature Weighting for Lazy Learning Algorithms," Data
Mining Perspective. Norwell. Mass.: Kluwer., pp.13-32., 1998.
[2] Q. Yang and J. Wu, "Keep It Simple: A Case-Base Maintenance Policy
Based on Clustering and Information Theory," Proceedings of the 13th
Biennial Conference of the Canadian Society on Computational Studies
of Intelligence: Advances in Artificial Intelligence, 102-114, 2000.
[3] R. Thomas and B. Roth, "Knowledge Maintenance of Case-Based
Reasoning SystemsÔÇöThe SIAM Methodology," volume 262 of
Dissertationen zur K├╝nstlichen Intelligenz. Akademische
Verlagsgesellschaft Aka GmbH / IOS Press., Berlin, Germany, 2003.
[4] S. Minton, "Qualitative Results Concerning the Utility of Explanation-
Based Learning," Artificial Intelligence, vol 42, pp. 363-391, 1990.
[5] B. Smyth and M. Keane, "Remembering to forget: A Competence-
Preserving Case Deletion Policy for Case-Based Reasoning Systems," In
Proceedings of the 13th International Joint Conference on Artificial
Intelligence., pp. 377-382, 1995.
[6] J. Zhu and Q. Yang, "Remembering to add: Competence-preserving
case-addition policies for case based reasoning," 1998.
[7] S. Massie, S. Craw, and N. Wiratunga, "Complexity-guided case
discovery for case based reasoning," In: The Twentieth National
Conference on Artificial Intelligence, pp. 216-221, 2005.
[8] K. Haouchine, B. Chebel-Morello, and N. Zerhouni, "Competence-
Preserving Case-Deletion Strategy for Case-base Maintenance.,"
Uncertainty, Similarity, andKnowledge Discovery in Case-Based
Reasoning workshop. 9th European Conference on CBR.
[9] G. Cao, S. Shiu, and X. Wang X, "A fuzzy-rough approach for case
base maintenance Cataltepe Z, Abu-mostafa YS, Magdon-ismail M
(1999) No free lunch for early stopping. Neural Computation, pp.
11:995-1009, 2001.
[10] R. W. Lucky, "Automatic equalization for digital communication," Bell
Syst. Tech. J., vol. 44, no. 4, pp. 547-588, Apr. 1965.
[11] M. Salam'o and E. Golobardes, "Global, local and mixed rough sets
case base maintenance techniques," In: Proceedings of the 6th Catalan
Conference on Artificial Intelligence, IOS Press, pp 127-134, 2004.
[12] N. Arshadi and I. Jurisica, "Maintaining Case-Based Reasoning
Systems: A Machine Learning Approach," Advances in Case-Based
Reasoning: Proc. Seventh European Conf., pp. 17-31, 2004.
[13] R. Pan, Q. Yang, J.J. Pan, and L. Li , "Competence Driven Case-Base
Mining," AAAI .2005, pp. 228-233, 2005.
[14] S.S. Wang and Yeung., "Transferring Case Knowledge to Adaptation
Knowledge: An Approach for Case-base Maintenance," Computational
Intelligence., 2001.
[15] C. Yang, R. Orchard, B. Farley, and M. Zaluski M, "Authoring Cases
from Free-Text Maintenance Data," Machine Learning and Data
Mining in Pattern Recognition , Springer Berlin, Heidelberg., 2004.
[16] N. Zhi-wei, L. Yu, and L. Feng-gang, "Case base maintenance based on
outlier data mining," Proc.4th Intl.Conf.on Machine Learning and
Cybernetics., Guangzhou., pp. 2,861-2,864, 2005.
[17] J. Daengdej, "Adaptable Case Base Reasoning Techniques For Dealing
With Highly Noise Cases," The University of New England, 1998.
[18] S. Bogaerts and D. Leake, "IUCBRF Lesson: Case Base
Maintenance Policies," 2005.
[19] B. Smyth, "Case-base maintenance," In Proceedings of the 11th
International Conference on Industrial and Engineering Applications of
Artificial Intelligence and Expert Systems, Springer-Verlag, 1998.
[1] D.W. Aha, "Feature Weighting for Lazy Learning Algorithms," Data
Mining Perspective. Norwell. Mass.: Kluwer., pp.13-32., 1998.
[2] Q. Yang and J. Wu, "Keep It Simple: A Case-Base Maintenance Policy
Based on Clustering and Information Theory," Proceedings of the 13th
Biennial Conference of the Canadian Society on Computational Studies
of Intelligence: Advances in Artificial Intelligence, 102-114, 2000.
[3] R. Thomas and B. Roth, "Knowledge Maintenance of Case-Based
Reasoning SystemsÔÇöThe SIAM Methodology," volume 262 of
Dissertationen zur K├╝nstlichen Intelligenz. Akademische
Verlagsgesellschaft Aka GmbH / IOS Press., Berlin, Germany, 2003.
[4] S. Minton, "Qualitative Results Concerning the Utility of Explanation-
Based Learning," Artificial Intelligence, vol 42, pp. 363-391, 1990.
[5] B. Smyth and M. Keane, "Remembering to forget: A Competence-
Preserving Case Deletion Policy for Case-Based Reasoning Systems," In
Proceedings of the 13th International Joint Conference on Artificial
Intelligence., pp. 377-382, 1995.
[6] J. Zhu and Q. Yang, "Remembering to add: Competence-preserving
case-addition policies for case based reasoning," 1998.
[7] S. Massie, S. Craw, and N. Wiratunga, "Complexity-guided case
discovery for case based reasoning," In: The Twentieth National
Conference on Artificial Intelligence, pp. 216-221, 2005.
[8] K. Haouchine, B. Chebel-Morello, and N. Zerhouni, "Competence-
Preserving Case-Deletion Strategy for Case-base Maintenance.,"
Uncertainty, Similarity, andKnowledge Discovery in Case-Based
Reasoning workshop. 9th European Conference on CBR.
[9] G. Cao, S. Shiu, and X. Wang X, "A fuzzy-rough approach for case
base maintenance Cataltepe Z, Abu-mostafa YS, Magdon-ismail M
(1999) No free lunch for early stopping. Neural Computation, pp.
11:995-1009, 2001.
[10] R. W. Lucky, "Automatic equalization for digital communication," Bell
Syst. Tech. J., vol. 44, no. 4, pp. 547-588, Apr. 1965.
[11] M. Salam'o and E. Golobardes, "Global, local and mixed rough sets
case base maintenance techniques," In: Proceedings of the 6th Catalan
Conference on Artificial Intelligence, IOS Press, pp 127-134, 2004.
[12] N. Arshadi and I. Jurisica, "Maintaining Case-Based Reasoning
Systems: A Machine Learning Approach," Advances in Case-Based
Reasoning: Proc. Seventh European Conf., pp. 17-31, 2004.
[13] R. Pan, Q. Yang, J.J. Pan, and L. Li , "Competence Driven Case-Base
Mining," AAAI .2005, pp. 228-233, 2005.
[14] S.S. Wang and Yeung., "Transferring Case Knowledge to Adaptation
Knowledge: An Approach for Case-base Maintenance," Computational
Intelligence., 2001.
[15] C. Yang, R. Orchard, B. Farley, and M. Zaluski M, "Authoring Cases
from Free-Text Maintenance Data," Machine Learning and Data
Mining in Pattern Recognition , Springer Berlin, Heidelberg., 2004.
[16] N. Zhi-wei, L. Yu, and L. Feng-gang, "Case base maintenance based on
outlier data mining," Proc.4th Intl.Conf.on Machine Learning and
Cybernetics., Guangzhou., pp. 2,861-2,864, 2005.
[17] J. Daengdej, "Adaptable Case Base Reasoning Techniques For Dealing
With Highly Noise Cases," The University of New England, 1998.
[18] S. Bogaerts and D. Leake, "IUCBRF Lesson: Case Base
Maintenance Policies," 2005.
[19] B. Smyth, "Case-base maintenance," In Proceedings of the 11th
International Conference on Industrial and Engineering Applications of
Artificial Intelligence and Expert Systems, Springer-Verlag, 1998.
@article{"International Journal of Information, Control and Computer Sciences:57964", author = "A. Lawanna and J. Daengdej", title = "Methods for Case Maintenance in Case-Based Reasoning", abstract = "Case-Based Reasoning (CBR) is one of machine
learning algorithms for problem solving and learning that caught a lot
of attention over the last few years. In general, CBR is composed of
four main phases: retrieve the most similar case or cases, reuse the
case to solve the problem, revise or adapt the proposed solution, and
retain the learned cases before returning them to the case base for
learning purpose. Unfortunately, in many cases, this retain process
causes the uncontrolled case base growth. The problem affects
competence and performance of CBR systems. This paper proposes
competence-based maintenance method based on deletion policy
strategy for CBR. There are three main steps in this method. Step 1,
formulate problems. Step 2, determine coverage and reachability set
based on coverage value. Step 3, reduce case base size. The results
obtained show that this proposed method performs better than the
existing methods currently discussed in literature.", keywords = "Case-Based Reasoning, Case Base Maintenance,Coverage, Reachability.", volume = "4", number = "1", pages = "117-9", }
