IEEE Trans Neural Netw Learn Syst. 2017 Dec;28(12):3045-3060. doi: 10.1109/TNNLS.2016.2607757. Epub 2016 Oct 11.
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 recognition-based 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.
传统的极限学习机 (ELM) 通过求解隐藏层激活矩阵的 Moore-Penrose 广义逆，并假设不同类型的错误分类具有相同的损失，从而分析确定输出权重以实现广义性能。这种假设在代价敏感识别任务中可能不成立，例如基于人脸识别的门禁系统，将陌生人误认作家庭成员可能比将家庭成员误认作陌生人造成更严重的灾难。尽管最近的代价敏感学习可以通过给定的代价矩阵来减少总损失，该矩阵量化了一种错误相对于另一种错误的严重程度，但在许多实际情况下，用户并不知道代价矩阵。鉴于这些问题，本文提出了一种进化代价敏感的 ELM，具有以下优点：1）据我们所知，它是进化代价敏感分类场景中第一个 ELM 的提议；2）它很好地解决了在代价敏感学习任务中如何定义代价矩阵的开放性问题；3）引入了一种进化回溯搜索算法来自适应地优化代价矩阵。在各种代价敏感任务中的实验表明了所提出方法的有效性，可提高约 5%-10%。
