期刊
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
卷 85, 期 -, 页码 68-78出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2017.03.008
关键词
Cost-sensitive learning; Deep neural network; Granular computing; Three-way decision
资金
- National Natural Science Foundation of China (NSFC) [71671086, 61473157, 71201076, 61403200, 71571148]
- Research Innovation Program for College Graduates of Jiangsu Province [KYZZ160025]
Three-way decision (3WD) models have been widely investigated in the fields of approximate reasoning and decision making. Recently, sequential 3WD models have attracted increasing interest, especially for image data analysis. It is essential to select an appropriate feature extraction and granulation method for sequential 3WD-based image data analysis. Among the existing feature extraction methods, deep neural networks (DNNs) have been considered widely due to their powerful capacity for representation. However, several important problems affect the application of DNN-based feature extraction methods to sequential 3WD. First, it takes a long time for a DNN to obtain an optimal feature representation. Second, most DNN algorithms are cost-blind methods and they assume that the costs of all misclassifications are the same, which is not the case in real-world scenarios. Third, DNN algorithms are two-way decision models and they cannot provide boundary decisions if sufficient information is not available. To address these problems, we propose a DNN-based sequential granular feature extraction method, which sequentially extracts a hierarchical granular structure from the input images. Based on the sequential multi-level granular features, a cost-sensitive sequential 3WD strategy is presented that considers the misclassification cost and test cost in different decision phases. Our experimental analysis validated the effectiveness of the proposed sequential DNN-based feature extraction method for 3WD. (C) 2017 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据