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Hybrid Decision-Making using Adaptive Technology

by Rodrigo Suzuki Okada, João José Neto
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 3
Year of Publication: 2012
Authors: Rodrigo Suzuki Okada, João José Neto
10.5120/6763-9040

Rodrigo Suzuki Okada, João José Neto . Hybrid Decision-Making using Adaptive Technology. International Journal of Computer Applications. 45, 3 ( May 2012), 38-45. DOI=10.5120/6763-9040

@article{ 10.5120/6763-9040,
author = { Rodrigo Suzuki Okada, João José Neto },
title = { Hybrid Decision-Making using Adaptive Technology },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 3 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number3/6763-9040/ },
doi = { 10.5120/6763-9040 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:36:41.666199+05:30
%A Rodrigo Suzuki Okada
%A João José Neto
%T Hybrid Decision-Making using Adaptive Technology
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 3
%P 38-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a hybrid approach to decision-making, capable of calibrating a trade-off between accuracy and response time by using multiple decision-making techniques to reach a solution of a decision problem. Each device employed by the decision-making system should also be able to learn from solutions suggested by other devices. This can be achieved by applying adaptive techniques, which make possible to change each device's behavior according to the input received. This process happens autonomously, without human interference.

References
  1. Crozier, R. and Ranyard, R. 1997. Cognitive process models and explanations of decision making. In Decision Making, Cognitive Models and Explanations, 5-20.
  2. Chittka, L. , Skorupski, P. and Raine, N. E. 2009. Speed-Accuracy Tradeoffs in Animal Decision Making. Trends in Ecology & Evolution, Vol. 24, No. 7, 400–407.
  3. Sol, H. G. , Takkenberg, C. A. T. and Robbé, P. F. V. 1985. Expert Systems and Artificial Intelligence in Decision Support Systems. In Proceedings of the Second Mini Euroconference. 17-20.
  4. Sprague, R. H. and Carlson, E. D. 1982. Building Effective Decision Support Systems. Englewood Cliffs: Prentice Hall Professional Technical Reference. 30-32.
  5. Hendrickx, I. and Bosch, A. 2005. Hybrid Algorithms with Instant-Based Classification. In Machine learning - ECML 2005, 158-169.
  6. Bui, T. and Lee, J. 2003. An Agent-Based Framework for Building Decision Support Systems. Decision Support Systems, Vol. 25, No. 3, 225-237.
  7. Kuncheva, L. I. 2009. Using Control Charts for Detecting Concept Change in Streaming Data. Technical Report. Bangor University.
  8. Arnott, D. 2004. Decision Support Systems Evolution: Framework, Case Study and Research Agenda. European Journal of Information Systems, Vol. 13, No. 4, 247-259.
  9. Pistori, H. 2003. Tecnologia Adaptativa em Engenharia de Computação: Estado da Arte e Aplicações. Doctoral Thesis. EPUSP (in Portuguese).
  10. Rocha, R. L. A. and Neto, J. J. 2000. Autômato adaptativo, limites e complexidade em comparação com máquina de Turing. In Proceedings of the second Congress of Logic Applied to Technology – LAPTEC 2000. 33-48 (in Portuguese).
  11. Neto, J. J. 2002. Adaptive Rule-Driven Devices - General Formulation and Case Study. Lecture Notes in Computer Science, Vol. 2494. 466-470.
  12. Castro Jr. , A. A. , Neto, J. J. and Pistori, H. 2007. Determinismo em Autômatos de Estados Finitos Adaptativos. Revista IEEE América Latina, Vol. 5, No. 7, 515-521 (in Portuguese).
  13. Pistori, H. , Neto, J. J. and Pereira, M. C. Adaptive Non-Deterministic Decision Trees: General Formulation and Case Study. INFOCOMP Journal of Computer Science, 2006, in press (in Portuguese).
  14. Tchemra, A. H. 2009. Tabela de Decisão Adaptativa na Tomada de Decisão Multicritério. Doctoral Thesis. EPUSP (in Portuguese).
  15. Yang, Y. and Webb, G. I. 2002. A Comparative Study of Discretization Methods for Naive-Bayes Classifiers. In Proceedings of PKAW 2002: The 2002 Pacific Rim Knowledge Acquisition Workshop. 159-173.
  16. Liu, W. and Chawla, S. 2011. Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets. In Proceedings of the 15th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 345-356.
  17. Shepard, D. 1968. A Two-Dimensional Interpolation Function for Irregularly-Spaced Data. In Proceedings of the 1968 ACM National Conference. 517-524.
  18. Li, C. and Li, H. 2010. A Survey of Distance Metrics for Nominal Attributes. Journal of Software, Vol. 5, No. 11, 1262-1269.
  19. Frank, A. and Asuncion, A. 2010. UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. http://archive. ics. uci. edu/ml.
  20. Keller, J. M. , Gray, M. R. and Givens, Jr. , J. A. 1985. A Fuzzy K-Nearest Neighbor Algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, No. 4, 580-585.
Index Terms

Computer Science
Information Sciences

Keywords

Decision-making Adaptive Device Case-based Reasoning Naive Bayes K-nearest Neighbors Decision Table

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