Abstract
Case-Based Reasoning Systems (CBR) are in constant evolution, as a result, this article proposes improving the retrieve and adaption stages through a different approach. A series of experiments were made, divided in three sections: a proper pre-processing technique, a cascade classification, and a probability estimation procedure. Every stage offers an improvement, a better data representation, a more efficient classification, and a more precise probability estimation provided by a Support Vector Machine (SVM) estimator regarding more common approaches. Concluding, more complex techniques for classification and probability estimation are possible, improving CBR systems performance due to lower classification error in general cases.
X. Blanco Valencia—This work is supported by Faculty of Engineering from University of Salamanca.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Leake, D.B.: CBR in context: the present and future. In: Case-Based Reasoning, Experiences, Lessons and Future Directions, pp. 1–30 (1996)
Kolodner, J.L.: Maintaining organization in a dynamic long-term memory. Cogn. Sci. 7(4), 243–280 (1983)
Abecker, A.: Corporate memories for knowledge management in industrial practice: prospects and challenges. J. Univ. Comput. Sci. 3(8), 929–954 (1997)
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)
Pal, S.K., Shiu, S.C.: Foundations of Soft Case-Based Reasoning, vol. 8. Wiley, Hoboken (2004)
Schank, R.C., Abelson, R.P.: Scripts, Plans, Goals and Understanding: An Inquiry into Human Knowledge Structures. Oxford, England (1977)
Koton, P.: Using experiences for in learning and problem solving. Engineering and Computer Science mMIT/LCS/TR-441 (1989)
Bareiss, R.: Exemplar Based Knowledge Acquisition: A Unified Approach to Concept Representati on, Classification, and Learning. Academic Press Professional Inc., San Diego (1989)
Anderson, J.R.: The Architecture of Cognition. Harvard University Press, Cambridge (1983)
Kolodner, J.L.: Maintaining organization in a dynamic long-term memory*. Cogn. Sci. 7(4), 243–280 (1983)
Shiu, S.C., Pal, S.K.: Case-based reasoning: concepts, features and soft computing. Appl. Intell. 21(3), 233–238 (2004)
Paz, J.F.D., Bajo, J., Vera, V., Corchado, J.M.: MicroCBR: a case-based reasoning architecture for the classification of microarray data. Appl. Soft Comput. 11(8), 4496–4507 (2011)
Paz, J.F.D., Bajo, J., López, V.F., Corchado, J.M.: Biomedic organizations: an intelligent dynamic architecture for KDD. Inf. Sci. 224, 49–61 (2013)
De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Case-based reasoning as a decision support system for cancer diagnosis: a case study. Int. J. Hybrid Intell. Syst. 6(2), 97–110 (2009)
Bichindaritz, I., Marling, C.: Case-based reasoning in the health sciences: what’s next? Artif. Intell. Med. 36(2), 127–135 (2006)
Juárez, J., Campos, M., Gomariz, A., Palma, J., Marin, R.: A reuse-based CBR system evaluation in critical medical scenarios. In: 21st International Conference on Tools with Artificial Intelligence, ICTAI 2009, pp. 261–268, November 2009
Montani, S.: How to use contextual knowledge in medical case-based reasoning systems: a survey on very recent trends. Artif. Intell. Med. 51(2), 125–131 (2011)
Krawczyk, B., Woźniak, M., Herrera, F.: On the usefulness of one-class classifier ensembles for decomposition of multi-class problems. Pattern Recogn. 48(12), 3969–3982 (2015)
Kang, S., Cho, S., Kang, P.: Multi-class classification via heterogeneous ensemble of one-class classifiers. Eng. Appl. Artif. Intell. 43, 35–43 (2015)
Lichman, M.: UCI Machine Learning Repository (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Blanco Valencia, X., Bastidas Torres, D., Piñeros Rodriguez, C., Peluffo-Ordóñez, D.H., Becerra, M.A., Castro-Ospina, A.E. (2018). Case-Based Reasoning Systems for Medical Applications with Improved Adaptation and Recovery Stages. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10813. Springer, Cham. https://doi.org/10.1007/978-3-319-78723-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-78723-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78722-0
Online ISBN: 978-3-319-78723-7
eBook Packages: Computer ScienceComputer Science (R0)