Abstract
Imperfect information inevitably appears in real situations for a variety of reasons. Although efforts have been made to incorporate imperfect data into learning and inference methods, there are many limitations as to the type of data, uncertainty and imprecision that can be handled. In this paper, we propose a classification and regression technique to handle imperfect information. We incorporate the handling of imperfect information into both the learning phase, by building the model that represents the situation under examination, and the inference phase, by using such a model. The model obtained is global and is described by a Gaussian mixture. To show the efficiency of the proposed technique, we perform a comparative study with a broad baseline of techniques available in literature tested with several data sets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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
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
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
Dubois D, Prade H (1980) Fuzzy sets and systems: theory and applications. Academic Press, Cambridge
Dubois D, Prade H (1988) Possibility theory: an approach to computerized processing of uncertainty. Plenum Press, New York
Duda RO, Hart PE, Stork DG (1991) Pattern classification. Wiley, NY
Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, Boston
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
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
Guan JW, Bell DA (1991) Evidence theory and its applications. Studies in computer science and artificial intelligence, vol 1. Elsevier Science Inc., North-Holland
Guan JW, Bell DA (1992) Evidence theory and its applications. Studies in computer science and artificial intelligence, vol 2. Elsevier Science Inc., North-Holland
Hu Y-C (2008) A novel fuzzy classifier with Choquet integral-based grey relational analysis for pattern classfication problems. Soft Comput 12:523–533
Janikow CZ (1996) Exemplar learning in fuzzy decision trees. In: Proceeding of the FUZZ-IEEE, New Orleans, USA, pp 1500–1505
Janikow CZ (1998) Fuzzy decision trees: issues and methods. IEEE Trans Man Systems Cybern 28:1–14
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
Klir GJ, Wierman MJ (1998) Uncertainty-based information. Physica-Verlag, Heidelberg
Kotsiantis SB, Kanellopoulos D, Pintelas PE (2006) Local boosting of decision stumps for regression and classification problems. J Comput 1(4):30–37
Mackay DJC (2003) Information theory, inference and learning algorithms. Cambridge University Press, Cambridge
McLachlan GJ, Krishnan T (1997) The EM algorithm and extensions. Wiley, New York
McLachlan GJ, Peel D (2000) Finite mixtures models. Wiley, New York
Mitra S, Pal SK (1995) Fuzzy multi-layer perceptron, inferencing and rule generation. IEEE Trans Neural Netw 6:51–63
Negoita CV, Ralescu DA (1975) Representation theorems for fuzzy concepts. Kybernetes 4:169–174
Quinlan JR (1993) C4.5: programs for machine learning. The Morgan Kaufmann Series in Machine Learning, California
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
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
Witten IH, Frank E (2000) Data mining. Morgan Kaufmann Publishers, San Francisco
Yager RR, Kacprzyk J, Fedrizzi M (eds) (1994) Advances in the Dempster–Shafer theory of evidence. Wiley, New York
Acknowledgments
This study was supported by the Project TIN2008-06872-C04-03 of the MICINN of Spain and European Fund for Regional Development. We also thank the Funding Program for Research Groups of Excellence with code 04552/GERM/06 granted by the “Fundación Séneca” (Spain).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Garrido, M.C., Cadenas, J.M. & Bonissone, P.P. A classification and regression technique to handle heterogeneous and imperfect information. Soft Comput 14, 1165–1185 (2010). https://doi.org/10.1007/s00500-009-0509-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-009-0509-y