Evolutionary Cost-sensitive Extreme Learning Machine and Subspace Extension

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Authors Lei Zhang, and David Zhang
Journal/Conference Name IEEE Transactions on Neural Networks and Learning Systems
Paper Category
Paper Abstract Conventional extreme learning machines 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 recognition based access control system, where misclassifying a stranger as a family member and allowed to enter the house may result in more serious disaster than misclassifying a family member as a stranger and not allowed to enter. 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 type of mistake, in many realistic cases the cost matrix is unknown to users. Motivated by these concerns, this paper proposes an evolutionary cost-sensitive extreme learning machine (ECSELM), with the following merits: 1) to our best knowledge, it is the first proposal of cost-sensitive ELM; 2) it well addresses the open issue of how to define the cost matrix in cost-sensitive learning tasks; 3) an evolutionary backtracking search algorithm is induced for adaptive cost matrix optimization. Extensively, an ECSLDA method is generalized by coupling with cost-sensitive subspace learning. Experiments in a variety of cost-sensitive tasks well demonstrate the efficiency and effectiveness of the proposed approaches, specifically, 5%~10% improvements in classification are obtained on several datasets compared with ELMs; the computational efficiency is also 10 times faster than cost-sensitive subspace learning methods.
Date of publication 2015
Code Programming Language MATLAB
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