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Multiple Instance Mamdani Fuzzy Inference
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Multiple Instance Mamdani Fuzzy Inference
Khalifa, Amine B.; Frigui, Hichem;
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A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.
Fuzzy inference;Mamdani FIS;Multiple Instance Learning;Algorithm fusion;
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L. A. Zadeh, A Theory of Approximate Reasoning (AR). Berkeley, CA: Electronics Research Laboratory, College of Engineering, University of California, 1977.

O. Cordon, "A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: designing interpretable genetic fuzzy systems," International Journal of Approximate Reasoning, vol. 52, no. 6, pp. 894-913, 2011. crossref(new window)

L. A. Zadeh, "Outline of a new approach to the analysis of complex systems and decision processes," IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 1, pp. 28-44, 1973.

E. H. Mamdani, "Application of fuzzy algorithms for control of simple dynamic plant," Proceedings of the Institution of Electrical Engineers, vol. 121, no. 12, pp. 1585-1588, 1974.

R. Jager, H. Verbruggen, and P. M. Bruijin, "Fuzzy inference in rule-based control systems," in Proceedings of 1st International Conference on Intelligent Systems Engineering, Edinburgh, Scotland, 1992, pp. 232-237.

C. C. Lee, "Fuzzy logic in control systems: fuzzy logic controller. II," IEEE Transactions on Systems, Man and Cybernetics, vol. 20, no. 2, pp. 419-435, 1990. crossref(new window)

J. Casillas, Interpretability Issues in Fuzzy Modeling. Berlin: Springer-Verlag, 2003.

C. Y. Chiu, H. C. Lin, and S. N. Yang, "A fuzzy logic CBIR system," in Proceedings of the 12th IEEE International Conference on Fuzzy Systems, St. Louis, MO, 2003, pp. 1171-1176.

A. A. Othman, H. R. Tizhoosh, and F. Khalvati, "EFIS: evolving fuzzy image segmentation," IEEE Transactions on Fuzzy Systems, vol. 22, no. 1, pp. 72-82, 2014. crossref(new window)

D. Y. Kim and H. M. Chung, "Image recognition by learning multi-valued logic neural network," International Journal of Fuzzy Logic and Intelligent Systems, vol. 2, no. 3, pp. 215-220, 2002. crossref(new window)

H. K. Kwan and L. Y. Cai, "Supervised fuzzy inference network for invariant pattern recognition," in Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems, Lansing, MI, 2000, pp. 850-854. crossref(new window)

J. S. Kim, "Prediction of user's preference by using fuzzy rule & RDB inference: a cosmetic brand selection," International Journal of Fuzzy Logic and Intelligent Systems, vol. 5, no. 4, pp. 353-359, 2005. crossref(new window)

A. Khalifa and H. Frigui, "Fusion of multiple algorithms for detecting buried objects using fuzzy inference," Proceedings of SPIE, vol. 9072, 2014. crossref(new window)

M. Y. Chen and D. Linkens, "Rule-base self-generation and simplification for data-driven fuzzy models," in Proceedings of the 10th IEEE International Conference on Fuzzy Systems, Melbourne, Australia, 2001, pp. 424-427. crossref(new window)

D. Z. Saletic, "On data-driven procedure for determining the number of rules in a Takagi-36 fuzzy model," in Proceedings of the International Conference on Computer as a Tool (EUROCON2005), Belgrade, Serbia 2005, pp. 1132-1135. crossref(new window)

P. Zikopoulos, D. DeRoos, K. Parasuraman, T. Deutsch, J. Giles, and D. Corrigan, Harness the Power of Big Data: The IBM Big Data Platform. New York, NY: McGraw-Hill, 2012.

"Flickr," 2014; Available

"Craigslist," 2014; Available

A. Sorokin and D. Forsyth, "Utility data annotation with Amazon Mechanical Turk," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Anchorage, AK, 2008, pp. 1-8. crossref(new window)

A. Torralba, B. C. Russell, and J. Yuen, "Labelme: online image annotation and applications," Proceedings of the IEEE, vol. 98, no. 8, pp. 1467-1484, 2010. crossref(new window)

I. J. Goodfellow, Y. Bulatov, J. Ibarz, S. Arnoud, and V. Shet, "Multi-digit number recognition from street view imagery using deep convolutional neural networks," in Proceedings of 2nd International Conference on Learning Representations (ICLR2014), Banff, Canada, 2014, pp. 1-13.

O. Maron, "Learning from ambiguity," Ph.D. dissertation, Massachusetts Institute of Technology, Cambridge, MA, 1998.

T. G. Dietterich, R. H. Lathrop, and T. Lozano-Perez, "Solving the multiple instance problem with axis-parallel rectangles," Artificial Intelligence, vol. 89, no. 1-2, pp. 31-71, 1997. crossref(new window)

C. Zhang, X. Chen, and W. B. Chen, "An online multiple instance learning system for semantic image retrieval," in Proceedings of 9th IEEE International Symposium on Multimedia Workshops (ISMW'07), Taichung, Taiwan, 2007, pp. 83-84. crossref(new window)

O. Maron and T. Lozano-Perez, "A framework for multiple-instance learning," in Proceedings of the 1997 Conference on Advances in Neural Information Processing Systems (NIPS'97), Cambridge, MA, 1998, pp. 570-576.

A. Karem and H. Frigui, "A multiple instance learning approach for landmine detection using ground penetrating radar," in Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, Canada, 2011, pp. 878-881. crossref(new window)

R. Rahmani and S. A. Goldman, "MISSL: Multipleinstance semi-supervised learning," in Proceedings of the 23rd International Conference on Machine Learning (ICML'06), Pittsburgh, PA, 2006, pp. 705-712. crossref(new window)

S. Ray and M. Craven, "Supervised versus multiple instance learning: an empirical comparison," in Proceedings of the 22nd International Conference on Machine Learning (ICML'05), Bonn, Germany, 2005, pp. 697-704. crossref(new window)

Y. Chen, J. Bi, and J. Z.Wang, "MILES: multiple-instance learning via embedded instance selection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 1931-1947, 2006. crossref(new window)

C. Yang, M. Dong, and F. Fotouhi, "Region based image annotation through multiple-instance learning," in Proceedings of the 13th Annual ACM International Conference on Multimedia, Singapore, 2005, pp. 435-438.

B. Babenko, M. H. Yang, and S. Belongie, "Robust object tracking with online multiple instance learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1619-1632, 2011. http: // crossref(new window)

P. L. Lanzi, W. Stolzmann, and S. W. Wilson, Learning Classifier Systems: From Foundations to Applications (LNCS vol.1813). Berlin: Springer, 2000.

L. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, no. 3, pp. 338-353, 1965. crossref(new window)

E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Human-Computer Studies, vol. 51, no. 2, pp. 135-147, 1999. crossref(new window)

E. H. Mamdani, "Application of fuzzy logic to approximate reasoning using linguistic synthesis," IEEE Transactions on Computers, vol. 26, no. 12, pp. 1182-1191, 1977.

T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116-132, 1985. crossref(new window)

R. R. Yager, "On the theory of bags," International Journal of General Systems, vol. 13, no. 1, pp. 23-37, 1986. crossref(new window)

K. T. Atanassov, "Intuitionistic fuzzy sets," Fuzzy Sets and Systems, vol. 20, no. 1, pp. 87-96, 1986. crossref(new window)

V. Torra, "Hesitant fuzzy sets," International Journal of Intelligent Systems, vol. 25, no. 6, pp. 529-539, 2010. crossref(new window)

D. Ramot, R. Milo, M. Friedman, and A. Kandel, "Complex fuzzy sets," IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 171-186, 2002. crossref(new window)

G. Cheng and J. Yang, "Complex fuzzy reasoning schemes," in Proceedings of 2010 3rd International Conference on Information and Computing (ICIC), Wuxi, China, 2010, pp. 29-32. crossref(new window)

V. G. Kaburlasos and A. Kehagias, "Fuzzy inference system (FIS) extensions based on the lattice theory," IEEE Transactions on Fuzzy Systems, vol. 2, no. 3, pp. 531-546, 2013. crossref(new window)

A. Mahnot and M. Popescu, "Fumil-fuzzy multiple instance learning for early illness recognition in older adults," in Proceedings of 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Brisbane, Australia, 2012, pp. 1-5. crossref(new window)

J. Casasnovas and G. Mayor, "Discrete t-norms and operations on extended multisets," Fuzzy Sets and Systems, vol. 159, no. 10, pp. 1165-1177, 2008. crossref(new window)

D. E. Tamir and A. Kandel, "Axiomatic theory of complex fuzzy logic and complex fuzzy classes," International Journal of Computers, Communications & Control, vol. 6, no. 3, pp. 562-576, 2011. crossref(new window)

J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River, NJ: Prentice Hall, 1997.

D. Dubois and H. M. Prade, Fuzzy Sets and Systems: Theory and Applications. New York, NY: Academic Press, 1980.

M. Xia and Z. Xu, "Hesitant fuzzy information aggregation in decision making," International Journal of Approximate Reasoning, vol. 52, no. 3, pp. 395-407, 2011. crossref(new window)

L. A. Zadeh, "Is there a need for fuzzy logic?," Information Sciences, vol. 178, no. 13, pp. 2751-2779, 2008. crossref(new window)

A. Karem and H. Frigui, "Fuzzy clustering algorithm of multiple instance data," in Proceedings of 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, Turkey, 2015, pp. 1-7. crossref(new window)

J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York, NY: Plenum Press, 1981.

H. Frigui and P. Gader, "Detection and discrimination of land mines in ground-penetrating radar based on edge histogram descriptors and a possibilistic k-nearest neighbor classifier," IEEE Transactions on Fuzzy Systems, vol. 17, no. 1, pp. 185-199, 2009. crossref(new window)