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Two decades of Ripple Down Rules research

Published online by Cambridge University Press:  01 June 2009

Debbie Richards*
Affiliation:
Computing Department, Division of Information and Communication Sciences, Macquarie University, Sydney, NSW 2109, Australia

Abstract

Ripple Down Rules (RDR) were developed in answer to the problem of maintaining medium to large rule-based knowledge systems. Traditional approaches to knowledge-based systems gave little thought to maintenance as it was expected that extensive upfront domain analysis involving a highly trained specialist, the knowledge engineer, and the time-poor domain expert would produce a complete model capturing what was in the expert’s head. The ever-changing, contextual and embrained nature of knowledge were not a part of the philosophy upon which they were based. RDR was a paradigm shift, which made knowledge acquisition and maintenance one and the same thing by incrementally acquiring knowledge as domain experts directly interacted with naturally occurring cases in their domain. Cases played an integral part of the acquisition process by motivating the capture of new knowledge, framing the context in which new knowledge would apply and ensuring that previously correctly classified cases remained so by requiring that the classification of the new case distinguish it from the system’s classification and be justified by features of the new case. RDR has moved beyond its first representation which handled single classification tasks within the domain of pathology to support multiple conclusions across a wide range of domains such as help-desk support, email classification and RoboCup and problem types including configuration, simulation, planning and natural language processing. This paper reviews the history of RDR research over the past two decades with a view to its future.

Type
Original Article
Copyright
Copyright © Cambridge University Press 2009

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References

Agnew, N. M., Ford, K. M., Hayes, P. J. 1997. Expertise in context: personally constructed, socially situated, and reality-relevant? In Expertise in Context: Human and Machine, Feltovich, P. J., Ford, K. M. & Hoffman, R. R. (eds). AAAI Press/MIT Press, 219244.Google Scholar
Bain, M., Muggleton, S. H. 1991. Non-monotonic learning. In Machine Intelligence Vol. 12 Towards an Automated Logic of Human Thought, Hayes, J. E., Michie, D. & Tyugu, E. (eds). Clarendon Press, 105119.CrossRefGoogle Scholar
Bekmann, J. P., Hoffmann, A. G. 2004. HeurEAKA—a new approach for adapting GAs to the problem domain. In Proceedings of the Pacific Rim Conference on Artificial Intelligence (PRICAI), Springer-Verlag, 361–372.Google Scholar
Bekmann, J. P., Hoffmann, A. G. 2005. Improved knowledge acquisition for high-performance heuristic search. In Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI-05, 41–46.Google Scholar
Beydoun, G., Hoffmann, A. 1997. Acquisition of search knowledge. In Knowledge Acquisition, Modeling and Management, Plaza, E. & Benjamins, R. (eds), 10th European Workshop, EKAW’97, Lecture Notes in Artificial Intelligence 1319, 116. Springer-Verlag.Google Scholar
Beydoun, G., Hoffmann, A. 2000. Incremental acquisition of search knowledge. Journal of Human-Computer Studies 52, 493530.CrossRefGoogle Scholar
Beydoun, G., Kwok, R., Hoffmann, A. 2000. Towards order independent incremental KA. In Proceedings of the 6th Pacific Knowledge Acquisition Workshop, The University of New South Wales, Sydney, Australia, 1–16.Google Scholar
Buchanan, B. 1986. Expert systems: working systems and the research literature. Expert Systems 3(1), 3251.CrossRefGoogle Scholar
Cao, T. M., Compton, P. 2005. A simulation framework for knowledge acquisition evaluation. In Twenty-Eighth Australasian Computer Science Conference (ACSC2005). Newcastle, 353–360.Google Scholar
Catlett, J. 1992. Ripple-Down-Rules as a mediating representation in interactive induction. In Proceedings of the Second Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop, Nov 9–13, Kobe, Japan, 155–170.Google Scholar
Chandrasekaran, B. 1986. Generic tasks in knowledge-based reasoning: high level building blocks for expert system design. IEEE Expert 1(3), 2330.CrossRefGoogle Scholar
Chapman, D. 1989. Penguins can make cake. AI Magazine 10, 4550.Google Scholar
Cho, W., Richards, D. 2004. E-Mail document categorization using BayesTH-MCRDR algorithm. In Proceedings of the Pacific Knowledge Acquisition Workshop 2004, Kang, B. H., Hoffmann, A., Yamaguchi, T. & Yeap, W. K. (eds). University of Tasmania Eprints Repository, Auckland, 226–240.Google Scholar
Cho, W. C., Richards, D. 2006. Automatic construction of a concept hierarchy to assist Web document classification. In Proceedings of 2nd International Conference on Information Management and Business (IMB.2006), February 13–16, Sydney, Australia.Google Scholar
Clancey, W. 1997. The conceptual nature of knowledge, situations and activity. In Expertise in Context: Human and Machine, Feltovich, P. J., Ford, K. M. & Hoffman, R. R. (eds). AAAI Press/MIT Press, 247291.Google Scholar
Compton, P. 2000. Simulating expertise. In Proceedings of the 6th Pacific International Knowledge Acquisition Workshop. School of Computer Science and Engineering, UNSW, Sydney, 51–70.Google Scholar
Compton, P., Cao, T., Kerr, J. 2004. Generalising incremental knowledge acquisition. In Proceedings of the Pacific Knowledge Acquisition Workshop 2004, Kang, B. H., Hoffmann, A., Yamaguchi, T. & Yeap, W. K. (eds). University of Tasmania Eprints Repository, Auckland, 44–53.Google Scholar
Compton, P., Jansen, R. 1988. Knowledge in context: a strategy for expert system maintenance. In Proceedings of the 2nd Australian Joint Artificial Intelligence Conference, Lecture Notes in Computer Science 406, 292–306. Springer.CrossRefGoogle Scholar
Compton, P. J., Jansen, R. 1990. A philosophical basis for knowledge acquisition. Knowledge Acquisition 2, 241257.CrossRefGoogle Scholar
Compton, P., Peters, L., Edwards, G., Lavers, T. G. 2006. Experience with ripple-down rules. Knowledge-Based System Journal 19(5), 356362.CrossRefGoogle Scholar
Compton, P., Preston, P., Kang, B. 1995. The use of simulated experts in evaluating knowledge acquisition. In Proceedings of the 9th Banff KA workshop on Knowledge Acquisition for Knowledge-Based Systems, Gaines, B. & Musen, M. (eds). Banff, Canada, 1–12.Google Scholar
Compton, P., Ramadan, Z., Preston, P., Le-Gia, T., Chellen, V., Mullholland, M. 1998. A trade-off between domain knowledge and problem solving method power. In 11th Banff Knowledge Acquisition Workshop (KAW) Proceedings, Gaines, B. & Musen, M. (eds). Banff, Canada, 1–19.Google Scholar
Compton, P., Richards, D. 2000. Generalising ripple-down rules (short paper). In Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management. Springer-Verlag, 380–386.Google Scholar
Compton, P., Srinivasan, A., Edwards, G., Malor, R., Lazarus, L. 1991. Knowledge base maintenance without a knowledge engineer. In Proceedings World Congress on Expert Systems, 1, 668–675. Pergmon Press, Orlando.Google Scholar
Dazeley, R., Kang, B. H. 2004. Detecting the knowledge frontier: an error predicting knowledge based system. In Proceedings of the Pacific Knowledge Acquisition Workshop 2004, Kang, B. H., Hoffmann, A., Yamaguchi, T. & Yeap, W. K. (eds). University of Tasmania Eprints Repository, Auckland, 241–253.Google Scholar
Drake, B., Beydoun, G. 2000. Predicate logic-based incremental knowledge acquisition. In Proceedings of the Sixth Pacific International Knowledge Acquisition Workshop, Compton, P., Hoffmann, A., Motoda, H. & Yamaguchi, T. (eds). Sydney, 71–88.Google Scholar
Edwards, G. 1996. Reflective Expert Systems in Clinical Pathology. MD Thesis, University of New South Wales.Google Scholar
Edwards, G., Compton, P. 1993. PEIRS: a pathologist maintained expert system for the interpretation of chemical pathology reports. Pathology 25, 2734.CrossRefGoogle Scholar
Finlayson, A., Compton, P. 2004. Incremental knowledge acquisition using RDR for soccer simulation. In Proceedings of the Pacific Knowledge Acquisition Workshop 2004, Kang, B. H., Hoffmann, A., Yamaguchi, T. & Yeap, W. K. (eds). University of Tasmania Eprints Repository, Auckland, 102–116.Google Scholar
Fujiwara, K., Yoshida, T., Motoda, H., Washio, T. 2002. Case generation method for constructing an RDR knowledge base. PRICAI 2002, 228–237.Google Scholar
Gaines, B. R., Compton, P. 1992. Induction of ripple-down rules. 5th Australian Conference on Artificial Intelligence, Hobart, Tasmania, 349–355.Google Scholar
Gaines, B. R., Compton, P. 1995. Induction of ripple-down rules applied to modeling large databases. Journal for Intelligent Information Systems 5(3), 211228.CrossRefGoogle Scholar
Gaines, B. R., Shaw, M. L. G. 1993. Knowledge acquisition tools based on personal construct psychology. Knowledge Engineering Review 8(1), 4985.CrossRefGoogle Scholar
Grosso, W. E., Eriksson, H., Fergerson, R. W., Gennari, H., Tu, S. W., Musen, M. 1999. Knowledge modelling at the millenium (The design and evolution of Protégé-2000). In Proceedings of the 12th Workshop on Knowledge Acquisition, Modeling and Management (KAW’99), 16–21 October, Banff, Alberta.Google Scholar
Helmbold, D., Sloan, R., Warmuth, M. K. 1989. Learning nested differences of intersection-closed concept classes. In Proceedings of the Second Annual Workshop on Computational Learning Theory, Rivest, R., Haussler, D. & Warmuth, M. K. (eds). Santa Cruz, California, United States, Morgan Kaufmann Publishers, 41–56.Google Scholar
Ho, V., Wobcke, W., Compton, P. 2003. EMMA: an E-mail Management Assistant. In IEEE/WIC International Conference on Intelligent Agent Technology, Liu, J., Faltings, B., Zhong, N., Lu, R. & Nishida, T. (eds). IEEE, Los Alamitos, CA, 67–74.Google Scholar
Hoffmann, A. G., Khan, A. S 2006. A new approach for the incremental development of retrieval functions for CBR. Applied Artificial Intelligence 20(6), 507542.CrossRefGoogle Scholar
Hoffmann, A. G., Kwok, R., Compton, P. 2001. Simulations for comparing knowledge acquisition and machine learning. In Proceedings of the Australian Joint Conference on Artificial Intelligence 2001. Springer-Verlag, 273–284.Google Scholar
Hoffmann, A., Pham, S. B. 2003. Towards topic-based summarization for interactive document viewing. In Proceedings of the 2nd International Conference on Knowledge Capture (Sanibel Island, FL, USA, October 23–25, 2003). K-CAP ’03, ACM, New York, NY, 28–35.Google Scholar
Ignizio, J. P. 1991. Introduction to Expert Systems: The Development and Implementation of Rule-Based Expert Systems. McGraw-Hill Inc.Google Scholar
Kang, B. 1996. Validating Knowledge Acquisition: Multiple Classification Ripple Down Rules. PhD Thesis, School of Computer Science and Engineering, University of NSW, Australia.Google Scholar
Kang, B., Compton, P. 1994. A maintenance approach to case based reasoning. In Advances in Case-Based Reasoning, Second European Workshop, EWCBR-94, Chantilly, France, November 7–10, 1994. Lecture Notes in Computer Science 984, 226–239. Springer.CrossRefGoogle Scholar
Kang, B., Compton, P., Preston, P. 1995. Multiple classification ripple down rules: evaluation and possibilities. In Proceedings of the 9th Banff Knowledge Acquisition for Knowledge Based Systems Workshop. Banff, Feb 26–March 3, 1, 17.1–17.20.Google Scholar
Kang, B., Yoshida, K., Motoda, H., Compton, P. 1997. A help desk system with intelligence interface. Applied Artificial Intelligence 11, 611631.CrossRefGoogle Scholar
Kerr, J., Compton, P. 2003. Toward generic model-based object recognition by knowledge acquisition and machine learning. In Proceedings of the Workshop on Mixed-Initiative Intelligent Systems, IJCAI 2003. Acapulco, Mexico.Google Scholar
Kim, M., Compton, P. 2004. Evolutionary document management and retrieval for specialized domains on the web. International Journal of Human Computer Studies 60(2), 201241.CrossRefGoogle Scholar
Kim, M., Compton, P., Kang, B. 1999. Incremental development of a web-based help desk system. The Fourth Australian Knowledge Acquisition Workshop. University of NSW, Sydney, 157–171.Google Scholar
Kivinen, J., Mannila, H., Ukkonen, E. 1993. Learning rules with local exceptions. In Proceedings of the European Conference on Computational Learning Theory (COLT). http://citeseer.ifi.unizh.ch/kivinen93learning.htmGoogle Scholar
Lee, M., Compton, P. 1995. From heuristic knowledge to causal. In Explanations Proceedings of Eighth Australian Joint Conference on Artificial Intelligence AI’95, Yao, X. (ed.). 13–17 November, Canberra, World Scientific, 83–90.Google Scholar
Mak, P., Byeong, H. K., Sammut, C., Kadous, W. 2004. Knowledge acquisition module for conversational agents. In Proceedings of the Pacific Knowledge Acquisition Workshop 2004, Kang, B. H., Hoffmann, A., Yamaguchi, T. & Yeap, W. K. (eds). University of Tasmania Eprints Repository, Auckland, 54–62.Google Scholar
Martinez-Bejar, R., Benjamins, R., Compton, P., Preston, P., Martin-Rubio, F. 1998a. A formal framework to build domain knowledge ontologies for Ripple-Down Rules-based systems. In Eleventh Workshop on Knowledge Acquisition, Modeling and Management (KAW’98), Gaines, B. & Musen, M. (eds). Banff, Alberta, Canada, 18–23 April, 2, SHARE.13.Google Scholar
Martinez-Bejar, R., Benjamins, R., Martin-Rubio, F. 1997. Designing operators for constructing domain knowledge ontologies. In Knowledge Acquisition, Modeling and Management, Plaza, E. & Benjamins, R. (eds). Lecture Notes in Artificial Intelligence 1319, 159173. Springer-Verlag.CrossRefGoogle Scholar
Martinez-Bejar, R., Shiraz, G. M., Compton, P. 1998b. Using Ripple Down Rules-based systems for acquiring fuzzy domain knowledge. In Eleventh Workshop on Knowledge Acquisition, Modeling and Management (KAW’98), Gaines, B. & Musen, M. (eds). Banff, Alberta, Canada, 18–23 April, 1, KAT.2.Google Scholar
McCreath, E., Kay, J., Crawford, E. 2006. IEMS—an approach that combines hand-crafted rules with learnt instance-based rules. Australian Journal of Intelligent Information Processing Systems 9(1), 49–63.Google Scholar
Misra, A., Sowmya, A., Compton, P. 2004. Incremental learning of control knowledge for lung boundary extraction. In Proceedings of the Pacific Knowledge Acquisition Workshop 2004, Kang, B. H., Hoffmann, A., Yamaguchi, T. & Yeap, W. K. (eds). University of Tasmania Eprints repository, Auckland, 211.Google Scholar
Mulholland, M., Preston, P., Haddad, P., Hibbert, B., Compton, P. 1996. Teaching a computer ion chromatography from a database of published methods. Journal of Chromatography 739, 1524.CrossRefGoogle Scholar
Mulholland, M., Preston, P., Sammut, C., Hibbert, B., Compton, P. 1993. An expert system for ion chromatography developed using machine learning and knowledge in context. In Proceedings of the 6th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. Edinburgh.Google Scholar
Newell, A. 1982. The knowledge level. Artificial Intelligence 18, 87127.CrossRefGoogle Scholar
Norman, D. A. 1986. Cognitive engineering. In User Centered System Design: New Perspectives on Human-Computer Interaction, Norman, D. A. & Draper, S. W. (eds). Lawrence Erlbaum Associates.CrossRefGoogle Scholar
Park, S., Kim, Y., Kang, B.-H. 2004. Personalized web document classification using MCRDR. In Proceedings of the Pacific Knowledge Acquisition Workshop 2004, Kang, B. H., Hoffmann, A., Yamaguchi, T & Yeap, W. K. (eds). University of Tasmania Eprints Repository, Auckland, 63–73.Google Scholar
Park, M., Wilson, L. S., Jin, J. S. 2001. Automatic extraction of lung boundaries by a knowledge-based method. In Selected Papers From the Pan-Sydney Workshop on Visualisation—Volume 2 (Sydney, Australia), Eades, P. & Weckert, J. (eds). ACM International Conference Proceeding Series, Australian Computer Society, 9, 1116.Google Scholar
Pham, S. B., Hoffmann, A. G. 2004a. Extracting positive attributions from scientific papers’. In Proceedings of the Discovery Science Conference 2004. Springer-Verlag, 169182.Google Scholar
Pham, S. B., Hoffmann, A. G. 2004b. KAFTIE: a new KA framework for building sophisticated information extraction systems. In Engineering Knowledge in the Age of the Semantic Web: 14th International Conference, EKAW’04. Springer-Verlag.Google Scholar
Pham, K. C., Sammut, C. 2005. RDRVision—Learning vision recognition with Ripple Down Rules. In Australasian Conference on Robotics and Automation, Sammut, C. (ed.). Australian Robotics and Automation Association, 7.Google Scholar
Prayote, A., Compton, P. 2006. Detecting anomalies and intruders. In AI 2006: Advances In Artificial Intelligence, 19th Australia Joint Conference on Artificial Intelligence. Hobart, Australia, Springer, 1084–1088.Google Scholar
Ramadan, Z., Mulholland, M., Hibbert, D. B., Preston, P., Compton, P., Haddad, P. R. 1998. Towards an expert system in ion-exclusion chromatography by means of multiple classification ripple-down rules. Journal of Chromatography A 804(1), 2935.CrossRefGoogle Scholar
Richards, D. 1998. The Reuse of Knowledge in Ripple Down Rules Knowledge Bases Systems. PhD Thesis, University of New South Wales.Google Scholar
Richards, D. 2000. The reuse of knowledge: a user-centred approach. International Journal of Human Computer Studies 52(3), 553579.CrossRefGoogle Scholar
Richards, D. 2003. Merging individual conceptual models of requirements. Special Issue on Model-Based Requirements Engineering for the International Journal of Requirements Engineering 8, 195205.Google Scholar
Richards, D. 2004. Addressing the ontology acquisition bottleneck through reverse ontological engineering. Journal of Knowledge and Information Systems 6, 402427.CrossRefGoogle Scholar
Richards, D., Compton, P. 1999. Revisiting Sisyphus I—an incremental approach to resource allocations using ripple-down rules. In 12th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Gaines, B., Kremer, R. & Musen, M. (eds). SRDG Publications, University of Calgary, 7-7.1–7-7.20.Google Scholar
Richards, D., Menzies, T. 1998. Extending the SISYPHUS III experiment from a knowledge engineering to a requirements engineering task. In 11th Workshop on Knowledge Acquisition, Modeling and Management (KAW’98), Gaines, B. & Musen, M. (eds). Banff, Canada, SRDG Publications, Departments of Computer Science, University of Calgary, 1, SIS-6.Google Scholar
Richards, D., Szilas, N. 2006. Training the training system. In Proceedings of 2006 International Conference on Intelligent User, January 29–February 1, Sydney, Australia.CrossRefGoogle Scholar
Satoh, K., Nakagawa, R. 2000. Discovering critical cases in case-based reasoning. In Online Proceedings of the Sixth International Symposium on Artificial Intelligence and Mathematics, Florida.Google Scholar
Scheffer, T. 1996. Algebraic foundations and improved methods of induction or rippledown rules. In Proceedings of the 2nd Pacific Rim Knowledge Acquisition Workshop, PKAW’1996. http://citeseerx.ist.psu.edu/viewdoc/summary? doi=10.1.1.1.7100, accessed August 13, 2008.Google Scholar
Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., Van De Velde, W., Weilinga, B. 1999. Knowledge Engineering and Management: The Common KADS Methodology. MIT Press.CrossRefGoogle Scholar
Schreiber, G., Weilinga, B., Breuker, J. (eds). 1993. KADS: a principled approach to knowledge-based system development. In Knowledge-Based Systems. Academic Press.Google Scholar
Shaw, M. L. G., Woodward, J. B. 1989. Mental models in the knowledge acquisition process. In Proceedings of Fourth Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff.Google Scholar
Shiraz, G., Sammut, C. 1997. Combining knowledge acquisition and machine learning to control dynamic systems. In Proceedings of the 15th International Joint Conference in Artificial Intelligence (IJCAI’97). Morgan Kaufmann, 908913.Google Scholar
Siromoney, A., Siromoney, R. 1993. Variations and local exceptions in inductive logic programming. Machine Intelligence 14, 213234.Google Scholar
Suryanto, H., Compton, P. 2000. Discovery of class relations in exception structured knowledge bases. In Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues, August 14–18, Ganter, B. & Mineau, G. W. (eds). Lecture Notes in Computer Science 1867, 113–126. Springer-Verlag.Google Scholar
Vazey, M. 2006. Stochastic foundations for the case-driven acquisition of classification rules. In 15th International Conference on Knowledge Engineering and Knowledge Management Managing Knowledge in a World of Networks (EKAW 2006), 2–6 October, Podebrady, Czech Republic.Google Scholar
Vazey, M., Richards, D. 2004. Achieving rapid knowledge acquisition in a high-volume call center. In Proceedings of the Pacific Knowledge Acquisition Workshop 2004, Kang, B. H., Hoffmann, A., Yamaguchi, T. & Yeap, W. K. (eds). University of Tasmania Eprints Repository, Auckland, 74–86.Google Scholar
Vazey, M., Richards, D. 2006a. A case-classification-conclusion IR-RDR approach to knowledge acquisition: applying a classification logic Wiki to the problem solving process. International Journal of Knowledge Management 2(1), 7288.CrossRefGoogle Scholar
Vazey, M., Richards, D. 2006b. Evaluation of the FastFIX Prototypes 5Cs CARD System. In Proceedings of the Pacific Knowledge Acquisition Workshop (PKAW 2006), in conjunction with The Eighth Pacific Rim International Conference on Artificial Intelligence, August 7–11, 2006, Guilin, China, 106–117.Google Scholar
Vere, S. A. 1980. Multilevel counterfactuals for generalizations of relational concepts and productions. Artificial Intelligence 14, 139164.CrossRefGoogle Scholar
Wada, T., Motoda, H., Washio, T. 2001. Knowledge acquisition from both human expert and data. In Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (April 16–18, 2001), Cheung, D. W., Williams, G. J. & Li, Q. (eds). Lecture Notes In Computer Science, 2035, 550–561. Springer-Verlag.Google Scholar
Wang, J.C., Boland, M., Graco, W., He, H. 1996. Use of ripple-down rules for classifying medical general practitioner practice profiles repetition. In Proceedings of Pacific Knowledge Acquisition Workshop PKAW’96, Compton, P., Mizoguchi, R., Motoda, H. & Menzies, T. (eds). October 23–25, Coogee, Australia, 333–345.Google Scholar
Wille, R. 1992. Concept lattices and conceptual knowledge systems. Computers and Mathematics Applications 23(6–9), 493515.Google Scholar
Wrobel, S. 1988. Automatic representation adjustment in an observational discovery system. In Proceedings of the 3rd European Working Session on Learning, Sleeman, D. (ed.). Pitman, 253262.Google Scholar
Yao, Y., Wang, F.-Y., Wang, J., Zeng, D. 2005. Rule + Exception strategies for security information analysis. IEEE Intelligent Systems 20(3), 5257.CrossRefGoogle Scholar
Yoshida, T., Motoda, H. 2005. Performance evaluation of fusing two different knowledge sources in Ripple Down Rules method. In Proceedings of the 2005 International Conference on Active Media Technology (AMT 2005), May 19–21, Brisbane, Australia, 69–74.Google Scholar
Yoshida, T., Wada, T., Motoda, H., Washio, T. 2004. Adaptive Ripple Down Rules method based on minimum description length principle. Intelligent Data Analysis 8(3), 239265.CrossRefGoogle Scholar