Combining Expert Strategies in Multimodal Classification

Abstract:

In multimodal classification, we look for a set of strategies for mining and exploiting the most informative modalities for a given situation. These strategies are computations performed by the algorithms. In this paper, we propose to consider strategies as advice given to an algorithm by “expert.” There can be several classification strategies. Each strategy makes different assumptions regarding the fidelity of a sensor modality and uses different data to arrive at its estimates. Each strategy may place different trust in a sensor at different times, and each may be better in different situations. In this paper, we introduce a novel algorithm for combining expert strategies to achieve robust classification performance in a multimodal setting. We provide experimental results using real world examples to demonstrate the efficacy of the proposed algorithm.

• Periodical: Applied Mechanics and Materials (Volume 590)
• Pages: 693-697
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.590.693
• Online since: June 2014
• Authors: Jing Peng*
• Keywords: Classification, Data Fusion, Machine Learning

References

[1] A. Ross and A.K. Jain, Multimodal biometrics: an overview, Proceedings of 12th European Signal Processing Conference, p.1221—1224, (2004).

[2] J. Peng, C. Barbu, G. Seetharaman, W. Fan, X. Wu, and K. Palaniappan, Shareboost: Boosting for multi-view learning with performance guarantees, in Proceedings of ECML, 2011, pp.597-612.

DOI: 10.1007/978-3-642-23783-6_38

[3] J. Kittler, Combining classifiers: A theoretical framework, Pattern Analysis and Applications, vol. 1, p.18—27, (1998).

[4] L.I. Kuncheva, J.C. Bezdek, and R.P.W. Duin, Decision templates for multiple classifier fusion: An experimental comparison, Pattern Recognition, vol. 34, p.299—314, (2001).

DOI: 10.1016/s0031-3203(99)00223-x

[5] L.I. Kuncheva, A theoretical study on six classifier fusion strategies, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp.281-286, (1997).

DOI: 10.1109/34.982906

[6] L.I. Kuncheva, C.J. Whitaker, C.A. Shipp, and R.P.W. Duin, Is independence good for combining classifiers?, Proceedings of the 15th International Conference on Pattern Recognition, vol. 2, pp.168-171, (2000).

DOI: 10.1109/icpr.2000.906041

[7] S. Sonnenburg, G. Ratsch, C. Schafer, and B. Scholkopf, Large scale multiple kernel learning, Journal of Machine Learning Research, vol. 7, pp.1531-1565, (2006).

[8] P. Auer, N. Cesa-Bianchi, and P. Fischer, Finite-time analysis of the multi-armed bandit problem, Machine Learning, vol. 47, p.235—256, (2002).

DOI: 10.1023/a:1013689704352

[9] P. Auer, N. Cesa-Bianchi, Y. Freund, and R. Schapire, The non-stochastic multi-armed bandit problem, SIAM Journal on Computing, vol. 32, no. 1, pp.48-77, (2002).

DOI: 10.1137/s0097539701398375

[10] R. Busa-Fekete and B. Kegl, Accelerating adaboost using ucb, in KDDCup (JMLR W&CP), 2009, pp.111-122.

[11] F.D. Mesmay, A. Rimmel, Y. Voronenko, and M. Puschel, Bandit-based optimization on graphs with application to library performance tuning, in Proceedings of International Conference on Machine Learning, 2009, p.729—736.

DOI: 10.1145/1553374.1553468

[12] J. Maturana, A. Fialho, F. Saubion, M. Schoenauer, and M. Sebag, Extreme compass and dynamic multi-armed bandits for adaptive operator selection, In Proceedings of IEEE ICEC}, 2009, pp.365-372.

DOI: 10.1109/cec.2009.4982970

[13] J.Y. Audibert, R. Munos, and C. Szepesvari, Exploration-exploitation tradeoff using variance estimates in multi-armed bandits, Theor. Comput. Sci., vol. 410, no. 19, p.1876—1902, (2009).

DOI: 10.1016/j.tcs.2009.01.016

[14] R. Busa-Fekete and B. Kegl, Fast boosting using adversarial bandits, in Proceedings of International Conference on Machine Learning, (2010).

[15] H. Robbins, Some aspects of the sequential design of experiments, Bulletin American Mathematical Society, vol. 55, p.527—535, (1952).

DOI: 10.1090/s0002-9904-1952-09620-8

[16] N. Cesa-Bianchi and G. Lugosi, Prediction, Learning, and Games. Cambridge University Press, (2006).

[17] C. Rudin, R. Schapire, and I. Daubechies, Precise statements of convergence for adaboost and arc-gv, Contemporary Mathematics, vol. 443, (2007).

DOI: 10.1090/conm/443/08559

[18] R.E. Schapire and Y. Singer, Improved boosting algorithms using confidence rated predictions, Machine Learning, vol. 3, no. 37, p.297—336, (1999).

DOI: 10.1145/279943.279960

[19] S.S.S. Crihalmeanu, A. Ross, and L. Hornak, "A protocol for multi-biometric data acquisition, storage and dissemination. Technical Report, Lane Department of Computer Science and Electrical Engineering, West Virginia University, (2007).

[20] L. Masek and P. Kovesi, MATLAB source code for biometric identification system based on iris patterns. Technical Report, University of Western Australia, (2003).

[21] H.W. Mewes, et al. Mips: a database for genomes and protein sequences, Nucleic Acids Research, vol. 28, no. 1, p.37—40, (2000).

DOI: 10.1093/nar/28.1.37