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Machine Vision, Image Recognition and Machine Learning

[Cost-sensitive Learning and Recognition]

Paper:

Lei Zhang* and David Zhang, Evolutionary Cost-Sensitive Extreme Learning Machine, IEEE Transactions on Neural Networks and Learning Systems, 2016, doi: 10.1109/TNNLS.2016.2607757. [paper]

Abstract:

Conventional extreme learning machines (ELMs) solve a Moore–Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification. The assumption may not hold in cost-sensitive recognition tasks, such as face recognitionbased access control system, where misclassifying a stranger as a family member may result in more serious disaster than misclassifying a family member as a stranger. Though recent cost-sensitive learning can reduce the total loss with a given cost matrix that quantifies how severe one type of mistake against another, in many realistic cases, the cost matrix is unknown to users. Motivated by these concerns, this paper proposes an evolutionary cost-sensitive ELM, with the following merits: 1) to the best of our knowledge, it is the first proposal of ELM in evolutionary cost-sensitive classification scenario; 2) it well addresses the open issue of how to define the cost matrix in cost-sensitive learning tasks; and 3) an evolutionary backtracking search algorithm is induced for adaptive cost matrix optimization. Experiments in a variety of cost-sensitive tasks well demonstrate the effectiveness of the proposed approaches, with about 5%–10% improvements.

Datasets:

Experiments:

References:

[1] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, nos. 1–3, pp. 489–501, 2006.

[2] L. Zhang and F. Tian, “Performance study of multilayer perceptrons in a low-cost electronic nose,” IEEE Trans. Instrum. Meas., vol. 63, no. 7, pp. 1670–1679, Jul. 2014.

[3] J. Lu and Y. P. Tan, “Cost-sensitive subspace learning for face recognition,” in Proc. IEEE Conf. CVPR, Jun. 2010, pp. 2661–2666.

[4] X. Y. Liu and Z. H. Zhou, “Learning with cost intervals,” in Proc. ACM SIGKDD, Washington, DC, USA, 2010, pp. 403–412.

[5] S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, “Graph embedding and extensions: A general framework for dimensionality reduction,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 1, pp. 40–51, Jan. 2007.