On combining classifiers through a fuzzy multicriteria decision making approach: Applied to natural textured images

Expert Systems with Applications

Volumn 36, Issue 3 PART 2, 2009, Pages 7262-7269

  Guijarro, María ^a   Pajares, Gonzalo ^b  

^a Centro Superior de Estudios Felipe II   (Spain)

^b UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

Author keywords

Classifier; Fuzzy; Multicriteria decision making; Textured images; Unsupervised

Indexed keywords

BAYESIAN NETWORKS; CLASSIFIERS; CONFORMAL MAPPING; IMAGE CLASSIFICATION; SELF ORGANIZING MAPS;

FUZZY; FUZZY MULTI-CRITERIA DECISION MAKINGS; LEARNING VECTOR QUANTIZATION; MULTI CRITERIA DECISION MAKING; NON-PARAMETRIC BAYESIAN; TEXTURED IMAGES; UNSUPERVISED; VALIDATION MEASUREMENTS;

DECISION MAKING;

EID: 58349091222     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.09.021     Document Type: Article

References (38)

1. Alexandre L.A., Campilho A.C., and Kamel M. On combining classifiers using sum and product rules. Pattern Recognition Letters 22 (2001) 1283-1289
2. Asuncion, A., & Newman, D. J. (2008). UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science. Available from .
3. Balasko, B., Abonyi, J., & Feil, B. (2008). Fuzzy clústering and data analysis toolbox for use with matlab. Hungary: Veszprem University. Available from .
4. Bezdek J.C. Pattern recognition with fuzzy objective function algorithms (1981), Kluwer, Plenum Press, New York
5. Cao, J., Shridhar, M., & Ahmadi, M. (1995). Fusion of classifiers with fuzzy integrals. In Proceedings of the 3th international conference on document analysis and recognition, ICDAR'95 (Vol. 1, pp. 108-111).
6. Chen C.T. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems 114 (2000) 1-9
7. Deng D., and Zhang J. Combining multiple precision-boosted classifiers for indoor-outdoor scene classification. Information Technology and Applications 1 4-7 (2005) 720-725
8. Drimbarean A., and Whelan P.F. Experiments in colour texture analysis. Pattern Recognition Letters 22 4 (2003) 1161-1167
9. Duda R.O., Hart P.E., and Stork D.S. Pattern classification (2000), Wiley
10. Duin R.P.W. On the choice of smoothing parameters for Parzen estimators of probability density functions. IEEE Transactions on Computers 25 (1976) 1175-1179
11. Fukunaga K., and Hummels M.D. Bayes error estimation using Parzen and K-NN procedures. IEEE Transactions on Pattern Analysis and Machine Intelligence 9 5 (1987) 634-643
12. Gu X., and Zhu Q. Fuzzy multi-attribute decision-making method based on eigenvector of fuzzy attribute evaluation space. Decision Support Systems 41 (2006) 400-410
13. Hanmandlu, M., Madasu, V. K., & Vasikarla, S. (2004). A fuzzy approach to texture segmentation. In Proceedings of the IEEE international conference on information technology: Coding and computing, ITCC'04, The Orleans, Las Vegas, Nevada, USA (pp. 636-642).
14. Haykin S. Neural networks: A comprehensive foundation (1994), Macmillan College Publishing Co, New York
15. Jain, A. K., & Ramaswami, M. D. (1988). Classifier design with Parzen windows. In E.S. Geselma, L.N. Kanal (Eds.), Pattern recognition and artificial intelligence toward an integration (pp. 211-218). Amsterdam, The Netherlands.
16. Kittler J., Hatef M., Duin R.P.W., and Matas J. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 3 (1998) 226-239
17. Kohonen T. Self-organization and associative memory (1989), Springer-Verlag, Berlin
18. Kohonen, T. (1990). Improved versions of learning vector quantizations. In International Join Conference on Neural Networks I (pp. 545-550).
19. Kohonen T. Self-organizing maps (1997), Springer-Verlag, Berlin
20. Kong, Z., & Cai, Z. (2007). Advances of research in fuzzy integral for classifier's fusion. In Proceedings of the 8th ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (Vol. 2, pp. 809-814).
21. Kumar S., Ghosh J., and Crawford M.M. Best-bases feature extraction for pairwise classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 39 7 (2001) 1368-1379
22. Kumar S., Ghosh J., and Crawford M.M. Hierarchical fusion of multiple classifiers for hyperspectral data analysis. Pattern Analysis and Applications 5 (2002) 210-220
23. Kuncheva L.I. "Fuzzy" vs. "non-fuzzy" in combining classifiers designed by boosting. IEEE Transactions on Fuzzy Systems 11 6 (2003) 729-741
24. Kuncheva L.I. Combining pattern classifiers: Methods and algorithms (2004), Wiley
25. Maillard P. Comparing texture analysis methods through classification. Photogrammetric Engineering and Remote Sensing 69 4 (2003) 357-367
26. Pandya A.S., and Macy R.B. Pattern recognition with neural networks in C++ (1996), IEEE Press, Boca Raton, FL
27. Partridge D., and Griffith N. Multiple classifier systems: Software engineered, automatically modular leading to a taxonomic overview. Pattern Analysis and Applications 5 (2002) 180-188
28. Parzen E. On estimation of a probability density function and mode. Annals of Mathematical Statistics 33 (1962) 1065-1076
29. Puig D., and García M.A. Automatic texture feature selection for image pixel classification. Pattern Recognition 39 11 (2006) 1996-2009
30. Randen T.J.H., and Husøy J.H. Filtering for texture classification: A comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence 21 4 (1999) 291-310
31. Rud R., Shoshany M., Alchanatis V., and Cohen Y. Application of spectral features' ratios for improving classification in partially calibrated hyperspectral imagery: A case study of separating mediterranean vegetation species. Journal Real-Time Image Processing 1 (2006) 143-152
32. Smith G., and Burns I. Measuring texture classification algorithms. Pattern Recognition Letters 18 (1997) 1495-1501
33. Valdovinos R.M., Sánchez J.S., and Barandela R. Dynamic and static weighing in classifier fusion. In: Marques J.S., Pérez de la Blanca N., and Pina P. (Eds). Pattern recognition and image analysis. Lecture notes in computer science (2005), Springer-Verlag, Berlin 59-66
34. Wagner T. Texture aalysis. Handbook of computer vision and applications. In: Jähne B., Hauβecker H., and Geiβler P. (Eds). Signal processing and pattern recognition Vol. 2 (1999), Academic Press
35. Wang W., and Fenton N. Risk and confidence analysis for fuzzy multicriteria decision making. Knowledge Based Systems 19 (2006) 430-437
36. Yager R.R. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions of System Man and Cybernetics 18 1 (1988) 183-190
37. Yu, H., Li, M., Zhang, H. J., & Feng, J. (2002). Color texture moments for content-based image retrieval. In Proceedings of the international conference on image processing (Vol. 3, pp. 24-28).
38. Zimmermann H.J. Fuzzy set theory and its applications (1991), Kluwer Academic Publishers, Norwell