INTELLECTUAL SUPPORT OF THE PROCESSES OF SEARCHING AND EXTRACTION OF PRECEDENTS IN CASE-BASED REASONING APPROACH
DOI:
https://doi.org/10.15588/1607-3274-2024-3-10Keywords:
situational management systems, rough set theory, case-based reasoning, case library, lower and upper approximation of the target setAbstract
Context. The situational approach is based on the real-time decision-making methods for solving current problem situation. An effective tool for implementing the concept of a situational approach is an experience-based technique that widely known as сasebased reasoning approach. Reasoning by precedents allows solving new (latest) problems using knowledge and accumulated experience of previously solved problems. Since cases (precedents) describing a scenario for solving a certain problem situation are stored in the case library, their search and retrieval directly determine the system response time. In these conditions, there is a need to find ways of solving an actual scientific and practical problem aimed at optimizing case searching and extracting processes. The object of the paper is the processes of searching and extracting of cases from the case library.
Objective. The purpose of the article is to improve the process of cases searching in CBR approach by narrowing down the set of cases permissible for solving the current target situation, and excluding from further analysis such cases that do not correspond to the given set of parameters of the target situation.
Method. The research methodology is based on the application of rough set theory methods to improve the decision-making procedure based on reasoning by precedents. The proposed two-stage procedure for narrowing the initial set of cases involves preliminary filtering of precedents whose parameter values belong to the given neighborhoods of the corresponding parameters of the target situation at the first stage, and additional narrowing of the obtained subset of cases by the methods of rough set theory at the second stage. The determination of the R-lower and R-upper approximations of a given target set of cases within the notation of rough set theory allows dividing (segmenting) the original set of cases available for solving the current problem stored in case library into three subgroups (segments). The search for prototype solutions can be performed among a selected subset of cases that can be accurately classified as belonging to a given target set; which with some degree of probability can be attributed to the given target set, or within the framework of the union of these two subsets. The third subset contains cases that definitely do not belong to the given target set and can be excluded from further consideration.
Results. The problem of presentation and derivation of knowledge based on precedents has been considered. The procedure for searching for precedents in case library has been improved in order to reduce the system response time required to find the solution closest to the current problem situation by narrowing the initial set of cases.
Conclusions. The case-based reasoning approach is received the further development by segmenting cases in terms of their belonging to a given target set of precedents uses methods of the rough set theory, then the search for cases is carried out within a given segment. The proposed approach, in contrast to the classic CBR framework, uses additional knowledge derived from obtained case segment; allows modeling the uncertainty regarding the belonging / non-belonging of a case to a given target set; removing from further consideration cases that do not correspond to a given target set.
References
Domariev V. V. Systema sytuatsiinogo upravlinnia: teoriia, metodologiia, rekomendatsii. Kyiv, Znannia Ukrainy, 2017, 347 p.
Perner P. In: Nyström I., Hernández Heredia Y., Milián Núñez V. (eds.) Case-based reasoning – methods, techniques, and applications, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2019. Lecture Notes in Computer Science. Cham, Springer, 2019, Vol. 11896, pp. 16–30. DOI: 10.1007/978-3-030-33904-3_2
Riesbeck C. K., Schank R. C. Inside case-based reasoning. New York, Psychology Press, 1989, 448 p.
Pal S. K., Shiu S. C. K. Foundation of soft case-based reasoning. New Jersey, John Wiley & Sons, Inc, 2004. 300 p.
Aamodt A., Plaza E. Case-based reasoning: fundamental issues, methodological variations and system approaches. AI Communications, 1994, Vol. 7(1), pp. 39–59.
Kowalski Z., Meler-Kapcia M., Zieliński S., Drewka M. CBR methodology application in an expert system for aided design ship’s engine room automation. Expert Systems with Applications, 2005, Vol. 29, Iss. 2, pp. 256–263. DOI: 10.1016/j.eswa.2005.03.002
Zhong Q.-Y., Zhang Y.-J., Qu X.-F., Ye X., Qu Y., Research on method of CBR and its application in emergency commanding and decision-making, Wireless Communications, Networking and Mobile Computing: 4th International Conference. Dalian, China, 12–14 October 2008: proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2008, pp. 1–4. DOI: 10.1109/WiCom.2008.2744
Ramos-Quintana F., Tovar-Sánchez E., Saldarriaga-Noreña H., Sotelo-Nava H., Sánchez-Hernández J.P., Castrejón-Godínez M-L. A CBR–AHP hybrid method to support the decision-making process in the selection of environmental management actions, Sustainability, 2019, Vol. 11(20): 5649. DOI: 10.3390/su11205649
Ramadhani M., Sihombing V., Yanris G. Application of the case based learning (CBR) method to diagnose conjunctivitis, SinkrOn, 2021, Vol. 6, pp. 176–182. DOI: 10.33395sinkron.v6i1.10908
Schmidt R., Montani S., Bellazzi R., Portinale L., Gierl L. Cased-based reasoning for medical knowledge-based systems, International Journal of Medical Informatics, 2001, Vol. 64, Iss. 2–3, pp. 355–367. DOI: 10.1016/S1386-5056(01)00221-0
Galushka M., Patterson D. Intelligent index selection for case-based reasoning. Knowledge-Based Systems, 2006, Vol. 19(8), pp. 625–638. DOI: 10.1016/j.knosys.2006.05.003
Sarkheyli A., Söffker D. Case indexing in case-based reasoning by applying situation operator model as knowledge representation model, IFAC-PapersOnLine, 2015, Vol. 48, Iss. 1, pp. 81–86. DOI: 10.1016/j.ifacol.2015.05.048
Beckmann N., Kriegel H. P., Schneider R., Seeger B. The R*-tree: an efficient and robust access method for points and rectangles, ACM SIGMOD: International Conference on Management of Data, Atlantic City, New Jersey, USA, 23–26 May 1990: proceedings. ACM Press. New York, NY, USA, 1990. P. 322–331.
Traina Jr. C., Traina A. J. M., Faloutos C., Seeger B. Fast indexing and visualization of metric datasets using Slimtrees, IEEE Transactions on Knowledge and Data Engineering, 2002, Vol. 14 (2), pp. 244–260. DOI: 10.1109/69.991715
Keane M. T. Analogical problem solving. Chichester, West Sussex, Ellis Horwood, 1988. 151 p.
Keane M. T. In: Wess S., Althoff K. D., Richter M. M. (eds.) Analogical asides on case-based reasoning. Topics in Case-Based Reasoning. Berlin, Springer-Verlag, 1994. pp. 21–32.
Medin D. L., Goldstone R. L., Gentner D. Respects for similarity, Psychological Review, 1993, Vol. 100, Iss. 2, pp. 254–278. DOI: 10.1037/0033-295X.100.2.254
Liao C.-K., Liu A., Chao Y.-S. A machine learning approach to case adaptation, Artificial Intelligence and Knowledge Engineering (AIKE): IEEE First International Conference, Laguna Hills, CA, USA, 26–28 September 2018: proceedings. Los Alamitos, California Washington, IEEE, 2018, pp. 106–109. DOI: 10.1109/AIKE.2018.00023.
Policastro C., Carvalho A., Delbem A. Automatic knowledge learning and case adaptation with a hybrid committee approach, Journal of Applied Logic, 2006, Vol. 4, Iss. 1, pp. 26–38. DOI: 10.1016/j.jal.2004.12.002
Leake D., Ye X., Crandall D. Supporting case-based reasoning with neural networks: an illustration for case adaptation, Combining Machine Learning and Knowledge Engineering (2021): AAAI Spring Symposium AAAI-MAKE 2021, Palo Alto, California, USA, 22–24 March 2021: proceedings. CEUR Workshop proceedings, Vol. 2846, 2021. URL:https://ceur-ws.org/Vol-2846/paper1.pdf
Hoekstra R. J. Ontology Representation: design patterns and ontologies that make sense. IOS Press, Incorporated, 2009, 248 p. DOI: 10.3233/978-1-60750-013-1-i
Pawlak Z. Rough sets, International Journal of Computer and Information Sciences, 1982, Vol. 11, pp. 341–356. DOI: 10.1007/BF01001956
Pawlak Z. Rough sets, theoretical aspects of reasoning about data. Boston, Kluwer Academic Publishers, 1991, 229 p.
Polkowski L., Artiemjew P. In: Smolinski T. G., Milanova M. G., Hassanien A. E. (eds.) Rough sets in data analysis: foundations and applications, Applications of Computational Intelligence in Biology. Studies in Computational Intelligence. Berlin, Heidelberg, Springer, 2008, Vol. 122, pp. 33–54. DOI: 10.1007/978-3-540-78534-7_2
Pawlak Z., Skowron A. In: Yeager R. E., Fedrizzi M., Kacprzyk J. (eds.) Rough membership function, Advaces in the Dempster-Schafer of Evidence. New York, Wiley, 1994, pp. 251–271
Suraj Z. An introduction to rough set theory and its applications. A tutorial, New Technologies for the Information Society: 1st International Computer Engineering Conference, Cairo, Egypt, 27–30 December 2004: proceedings. URL:https://www.researchgate.net/publication/242215208_An_Introduc-tion_to_Rough_Set_Theory_and_Its_Applications_A_tutorial
Jun Z., Zhou Y. New heuristic method for data discretization based on rough set theory, The Journal of China Universities of Posts and Telecommunications, 2009, Vol. 16(6), pp. 113–120.
Lindstrom J., Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk, Diabetes Care, 2003, Vol. 26(3), pp. 725–731. DOI: 10.2337/diacare.26.3.725
Margret Anouncia S., Clara Madonna L. J., Jeevitha P., Nandhini R. T. Design of a diabetic diagnosis system using rough sets, Cybernetics and Information Technologies, 2013, Vol. 13(3), pp. 124–139. DOI: 10.2478/cait-2013-0030
Cuzzolin F. A geometric approach to the theory of evidence, Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2007, Vol. 38(4), pp. 522–534. DOI: 10.1109/TSMCC.2008.919174
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