A classification and regression technique to handle heterogeneous and imperfect information

Soft Computing

Volumn 14, Issue 11, 2010, Pages 1165-1185

  Garrido, M Carmen ^a   Cadenas, Jose M ^a   Bonissone, Piero P ^b  

^a UNIVERSITY OF MURCIA   (Spain)

^b GE GLOBAL RESEARCH   (United States)

Author keywords

Dempster Shafer theory; Evidence theory; Gaussian mixture models; Handling imperfect observations; Inductive inference; Inductive learning

Indexed keywords

DEMPSTER-SHAFER THEORY; EVIDENCE THEORIES; GAUSSIAN MIXTURE MODEL; INDUCTIVE INFERENCE; INDUCTIVE LEARNING;

GAUSSIAN DISTRIBUTION;

FORMAL LOGIC;

EID: 77953916654     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-009-0509-y     Document Type: Article

References (27)

1. Asuncion A, Newman DJ (2007) UCI machine learning repository (http://www. ics. uci. edu/mlearn/MLRepository. html). University of California, School of Information and Computer Science, Irvine.
2. Cadenas JM, Garrido MC (2002) Imperfección Explícita versus adaptación de la información en MFGN extendido, In: Proceedings of the XI Congreso Español sobre Tecnologías y Lógica Fuzzy, León, Spain, pp 547-552.
3. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39(19): 1-38.
4. Dubois D, Prade H (1980) Fuzzy sets and systems: theory and applications. Academic Press, Cambridge.
5. Dubois D, Prade H (1988) Possibility theory: an approach to computerized processing of uncertainty. Plenum Press, New York.
6. Duda RO, Hart PE, Stork DG (1991) Pattern classification. Wiley, NY.
7. Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, Boston.
8. Garrido MC, Cadenas JM, Ruiz A (1999) Visual object detection using fuzzy concepts. In: Proceedings of the International ICSC Congress on computational intelligence methods and applications, Rochester, USA, pp 1-6.
9. Grabisch M, Disport F (1992) A comparison of some methods of fuzzy classification on real data. In: Proceedings of the 2nd international conference on fuzzy logic and neural network. Iizuka, Japan, pp 659-662.
10. Guan JW, Bell DA (1991) Evidence theory and its applications. Studies in computer science and artificial intelligence, vol 1. Elsevier Science Inc., North-Holland.
11. Guan JW, Bell DA (1992) Evidence theory and its applications. Studies in computer science and artificial intelligence, vol 2. Elsevier Science Inc., North-Holland.
12. Hu Y-C (2008) A novel fuzzy classifier with Choquet integral-based grey relational analysis for pattern classfication problems. Soft Comput 12: 523-533.
13. Janikow CZ (1996) Exemplar learning in fuzzy decision trees. In: Proceeding of the FUZZ-IEEE, New Orleans, USA, pp 1500-1505.
14. Janikow CZ (1998) Fuzzy decision trees: issues and methods. IEEE Trans Man Systems Cybern 28: 1-14.
15. Klir GJ (1992) Probabilistic versus possibilistic conceptualization of uncertainty. In: Ayyub BM, Gupta MM, Kanal LN (eds) Analysis and management of uncertainty: theory and applications. Elsevier, Amsterdam, pp 13-25.
16. Klir GJ, Wierman MJ (1998) Uncertainty-based information. Physica-Verlag, Heidelberg.
17. Kotsiantis SB, Kanellopoulos D, Pintelas PE (2006) Local boosting of decision stumps for regression and classification problems. J Comput 1(4): 30-37.
18. Mackay DJC (2003) Information theory, inference and learning algorithms. Cambridge University Press, Cambridge.
19. McLachlan GJ, Krishnan T (1997) The EM algorithm and extensions. Wiley, New York.
20. McLachlan GJ, Peel D (2000) Finite mixtures models. Wiley, New York.
21. Mitra S, Pal SK (1995) Fuzzy multi-layer perceptron, inferencing and rule generation. IEEE Trans Neural Netw 6: 51-63.
22. Negoita CV, Ralescu DA (1975) Representation theorems for fuzzy concepts. Kybernetes 4: 169-174.
23. Quinlan JR (1993) C4. 5: programs for machine learning. The Morgan Kaufmann Series in Machine Learning, California.
24. Ruiz A, López de Teruel PE, Garrido MC (1998) Probabilistic inference from arbitrary uncertainty using mixtures of factorized generalized Gaussians. J Artif Intell Res 9: 167-217.
25. Ruthven I, Lalmas M (2002) Using Dempster-Shafer's theory of evidence to combine aspects of information use. J Intell Inf Syst 19(3): 267-301.
26. Witten IH, Frank E (2000) Data mining. Morgan Kaufmann Publishers, San Francisco.
27. Yager RR, Kacprzyk J, Fedrizzi M (eds) (1994) Advances in the Dempster-Shafer theory of evidence. Wiley, New York.