Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79810
Title: Evolutionary cost-sensitive extreme learning machine
Authors: Zhang, L
Zhang, D 
Keywords: Classification
Cost matrix
Cost-sensitive learning
Extreme learning machine (ELM)
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, Dec. 2017, v. 28, no. 12, p. 3045-3060 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
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.
URI: http://hdl.handle.net/10397/79810
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2016.2607757
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