Evaluation of the mini-models robustness to data uncertainty with the application of the information-gap theory

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7895 LNAI, Issue PART 2, 2013, Pages 230-241

  Pluciński, Marcin ^a  

^a WEST POMERANIAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

data uncertainty; information gap theory; interval analysis; local regression; Mini model

Indexed keywords

DATA UNCERTAINTY; INFORMATION-GAP; INTERVAL ANALYSIS; LOCAL REGRESSION; MINI-MODEL;

ARTIFICIAL INTELLIGENCE; SOFT COMPUTING; UNCERTAINTY ANALYSIS;

QUALITY CONTROL;

EID: 84884375802     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38610-7_22     Document Type: Conference Paper

References (19)

1. Atkeson, C.G., Moore, A.W., Schaal, S.A.: Locally weighted learning. Artificial Intelligence Review 11, 11-73 (1997)
2. Ben-Haim, Y.: Set-models of information-gap uncertainty: axioms and an inference scheme. Journal of the Franklin Institute 336, 1093-1117 (1999)
3. Ben-Haim, Y.: Information-gap decision theory: decisions under severe uncertainty. Academic Press, New York (2001)
4. Ben-Haim, Y.: Uncertainty, probability and information-gaps. Reliability Engineering and System Safety 85, 249-266 (2004)
5. Cichosz, P.: Learning systems. WNT Publishing House, Warsaw (2000) (in polish)
6. Duch, W.: Uncertainty of data, fuzzy membership functions, and multilayer perceptrons. IEEE Transactions on Neural Networks 16(1), 10-23 (2005)
7. Hand, D., Mannila, H., Smyth, P.: Principles of data mining. The MIT Press (2001)
8. Kordos, M., Blachnik, M., Strzempa, D.: Do We Need Whatever More Than k-NN? In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part I. LNCS, vol. 6113, pp. 414-421. Springer, Heidelberg (2010)
9. Moore, A.W., Atkeson, C.G., Schaal, S.A.: Memory-based learning for control. Technical Report CMU-RI-TR-95-18, Carnegie-Mellon University, Robotics Institute (1995)
10. Moore, R.E., Kearfott, R.B., Cloud, M.J.: Introduction to interval analysis. Society for Industrial and Applied Mathematics, Philadelphia (2009)
11. Piegat, A.: Fuzzy modeling and control. Physica Verlag, Heidelberg (2001)
12. Piegat, A., Wa̧sikowska, B., Korzeń, M.: Application of the self-learning, 3-point mini-model for modeling of unemployment rate in Poland. Studia Informatica, University of Szczecin (2010) (in Polish)
13. Piegat, A., Wa̧sikowska, B., Korzeń, M.: Differences between the method of mini-models and the k -nearest neighbors on example of modeling of unemployment rate in Poland. In: Proceedings of 9th Conference on Information Systems in Management, pp. 34-43. WULS Press, Warsaw (2011)
14. Pierce, S.G., Ben-Haim, Y., Worden, K., Manson, G.: Evaluation of neural network robust reliability using information-gap theory. IEEE Transactions on Neural Networks 17(6), 1349-1361 (2006)
15. Plucinski, M.: Application of data with missing attributes in the probability RBF neural network learning and classification. In: Sołdek, J., Drobiazgiewicz, L. (eds.) Artificial Intelligence and Security in Computing Systems: 9th International Conference ACS 2002: Proceedings, pp. 63-72. Kluwer Academic Publishers, Boston (2003)
16. Pluciński, M.: Mini-models - local regression models for the function approximation learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 160-167. Springer, Heidelberg (2012)
17. Rump, S.M.: INTLAB - INTerval LABoratory. In: Developements in Reliable Computing, pp. 77-104. Kluwer Academic Publishing, Dordrecht (1999)
18. Wasserman, P.D.: Advanced methods in neural computing. Van Nostrand Reinhold, New York (1993)
19. Wright, W.A.: Bayesian approach to neural network modeling with input uncertainty. IEEE Transactions on Neural Networks 10(6), 1261-1270 (1999)