Experience-consistent modeling: Regression and classification problems

Automatica

Volumn 45, Issue 2, 2009, Pages 449-455

  Pedrycz, Witold ^a,b   Rai, Partab ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

Data; Experience consistency; Knowledge based guidance; Knowledge based regularization; Linear regression; Pattern classification; System identification

Indexed keywords

LEARNING SYSTEMS; REGRESSION ANALYSIS; SEMICONDUCTOR DEVICE MODELS;

DATA; EXPERIENCE CONSISTENCY; KNOWLEDGE-BASED GUIDANCE; KNOWLEDGE-BASED REGULARIZATION; PATTERN CLASSIFICATION; SYSTEM IDENTIFICATION;

KNOWLEDGE BASED SYSTEMS;

EID: 58349096502     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2008.07.010     Document Type: Article

References (21)

1. Bakken D.E., Parameswaran R., Blough D.M., Franz A.A., and Palmer T.J. Data obfuscation: Anonymity and desensitization of usable data sets. IEEE Security and Privacy 2 (2004) 34-41
2. Bates J.M., and Granger C.W.J. The combination of forecasts. Operations Research Quarterly 20 (1969) 451-468
3. Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In Proceedings of the conference on computational learning theory (pp. 92-100)
4. Breiman L. Bagging predictors. Machine Learning 24 3 (1996) 123-140
5. Cao S.G., Rees N.W., and Feng G. Analysis and design for a class of complex control systems, Part I and Part II. Automatica 33 6 (1997) 1017-1028 (1997) 33 (6) 1029-1039
6. Collins, M., & Singer, Y. (1999). Unsupervised models for named entity classification. In Conf. name of conference EMNLP, 6 (pp. 13-49)
7. Cox D.R., and Snell E.J. Analysis of binary data (1989), Chapman & Hall, London
8. Duin R.P.W., and Tax D.M.J. In: Kittler J., and Roli F. (Eds). Experiments with classifier combining rules. Lecture notes in computer science Vol. 1857 (2000), Springer 16-29
9. Fasol K.H., and Jörgl H.P. Principles of model building and identification. Automatica 16 5 (1980) 505-518
10. Jain A., Duin R., and Mao J. Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000) 4-37
11. Johnson E., and Kargupta H. Collective hierarchical clustering from distributed heterogeneous data. In: Zaki M., and Ho C. (Eds). Large-scale parallel KDD systems. Lecture notes in computer science Vol. 1759 (1999), Springer-Verlag 221-244
12. Ho T.K., Hull J.J., and Srihari J.N. Decision combination in multiple classifier systems. IEEE Transactions of Pattern Analysis and Machine Intelligence 16 (1994) 66-75
13. Krishnamurti T.N., et al. Improved weather and seasonal climate forecasts from multi-model superensemble. Science (1999) 1548-1550
14. Kumar M., Stoll N., and Stoll R. An energy-gain bounding approach to robust fuzzy identification. Automatica 42 5 (2006) 711-721
15. Lee H., Jo H., and Han I. Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Systems Applications 13 (1997) 97-108
16. Lee G., Sung T.K., and Chang N. Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction. Journal of Management Information Systems 16 (1999) 63-85
17. Pagowski, M. et al. A simple method to improve ensemble-based ozone forecasts, Geophysical Research Letters., 32, Lo7814. doi:10.1029/2004GL022305
18. Pedrycz W. Knowledge-based clustering: From data to information granules (2005), J. Wiley, Hoboken, NJ
19. Pedrycz W., and Gomide F. Fuzzy systems engineering (2007), J. Wiley, Hoboken, NJ
20. Pedrycz W., and Vukovich G. On elicitation of membership functions. IEEE Transactions on Systems, Man, and Cybernetics, Part B 32 (2002) 761-767
21. Zadeh L.A. Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 90 (1997) 111-117