Application of Fuzzy Numbers to the Assessment of CBR Systems
DOI:
https://doi.org/10.6000/2371-1647.2016.02.05Keywords:
Analogical Reasoning (AR), Case-Based Reasoning (CBR), Fuzzy Logic (FL), Centre of Gravity (COG) Defuzzification Technique, Triangular and Trapezoidal Fuzzy Numbers (TFNs and TpFNs), GPA index.Abstract
Case-Based Reasoning (CBR) is the process of solving problems by properly adapting the solutions of similar (analogous) problems solved in the past. As an Artificial Intelligence’s method CBR has become recently very popular to information managers increasing the effectiveness and reducing the cost of various human activities by substantially automated processes, such as diagnosis, scheduling, design, etc. In this paper a combination is utilized of the Centre of Gravity defuzzification technique and of the Fuzzy Numbers for assessing the effectiveness of CBR systems. Our new fuzzy assessment approach is validated by comparing its outcomes in our applications with the corresponding outcomes of two traditional assessment methods, the calculation of the mean values and the GPA index.
References
Holyoak KJ. The pragmatics of analogical transfer, in: Bower GH, Ed. The psychology of learning and motivation, Academic Press, New York, USA, 1985; Vol. 19: pp. 59-87. http://dx.doi.org/10.1016/s0079-7421(08)60524-1
Voskoglou MGr, Salem A-B. Analogy-Based and Case-Based Reasoning: Two Sides of the Same Coin. International Journal of Applications of Fuzzy Sets and Artificial Intelligence 2014; 4: 7-18.
Veloso MM, Carbonell J. Derivational analogy in PRODIGY. Machine Learning 1993; 10(3): 249-278. http://dx.doi.org/10.1023/A:1022686910523
Hall RP. Computational approaches to analogical reasoning: A comparative analysis. Artificial Intelligence 1989; 39(1): 39-120. http://dx.doi.org/10.1016/0004-3702(89)90003-9
Kedar-Cabelli S. Analogy – from a unified perspective, in Helman DH, Ed. Analogical Reasoning, Kluwer Academic, 1988; pp. 65-103.
Silvana Q, Pedro B, Steen A. Proceedings of 8th Conference on Artificial Intelligence in Medicine in Europe (AIME), Springer, Cascais, Portugal, 2001.
Hinkle D, Toomey C. Applying Case-Based Reasoning to Manufacturing. AI Magazine 1995; 65-73.
Hans-Dieter, Salem AB, El Bagoury BM. Ideas of Case-Based Reasoning for Keyframe Technique, Proceedings of the 16th International Workshop on the Concurrency Specification and Programming, Logow, Warsa, Poland, 2007; pp. 100-106.
Rissland EL, Danials JJ. A Hybrid CBR-IR Approach to Legal Information Retrieval, Proceedings of the Fifth International Conference on Artificial Intelligence and Law (ICAIL-95), College Park, MD, 1995; pp. 52-61. http://dx.doi.org/10.1145/222092.222125
Salem A-BM, Baeshen N. Artificial Intelligence Methodologies for Developing Decision Aiding Systems, Proceedings of 5th International Conference, Integrating Technology and Human Decisions: Global Bridges into the 21st Century, Decision Sciences Institute, Athens, Greece, 1999; pp. 168-170.
Voskoglou MGr. Case-Based Reasoning: History, methodology and Development Trends, in Leeland, A. M. (Ed.), Case-Based Reasoning: Processes, Suitability and Applications, Nova Publishers, NY, 2011; Chapter 3: 59-76.
Voskoglou MGr. A stochastic model for Case-Based Reasoning, Journal of Mathematical Modelling and Application (Blumenau University, Brazil) 2010; 3: 33-59.
Voskoglou MGr. Fuzzy sets in Case-Based Reasoning, Fuzzy Systems and Knowledge Discovery, IEEE Computer Society, 2009; Vol. 6: pp. 252-256.
Voskoglou MGr. Evaluating the Effectivness of a CBR System: A Fuzzy Logic Approach. American Journal of Computational and Applied Mathematics 2015; 5(2): 27-32.
Voskoglou MGr, Subbotin IYa. Fuzzy Models for Learning Assessment. Turkish Journal of Fuzzy Systems 2014; 5(2): 100-113.
Zadeh LA. Fuzzy Sets. Information and Control 1965; 8: 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
Klir GJ, Folger TA. Fuzzy Sets, Uncertainty and Information, Prentice-Hall, London, 1988.
Kaufmann A, Gupta M. Introduction to Fuzzy Arithmetic, Van Nostrand Reinhold Company, New York, 1991.
Sakawa M. Fuzzy Sets and Interactive Multiobjective Optimization, Plenum press, NY and London, 1993. http://dx.doi.org/10.1007/978-1-4899-1633-4
Theodorou J. Introduction to Fuzzy Logic, Tzolas Publications, Thessaloniki, Greece, 2010; (in Greek language).
Voskoglou M Gr. Use of the Triangular Fuzzy Numbers for Student Assessment (Revised), arXiv: 1507.03257, [cs.AI].
Wikipedia, Center of mass: A system of particles, retrieved on October 10, 2014 from: http://en.wikipedia.org/wiki/ Center_of_mass#A_system_of_particles
Swinburne.edu. Grade Point Average Assessment, retrieved on October 15, 2014 from: http://www.swinburne.edu.au/ studentadministration/assessment/gpa.html
Downloads
Published
How to Cite
Issue
Section
License
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
