期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 38, 期 11, 页码 2335-2341出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2015.2430325
关键词
Multi-class classification; error-correcting output coding (ECOC); (joint) binary classifier learning; relationship
资金
- National Natural Science Foundation of China [61422204, 61473149, 61375057]
- Jiangsu Natural Science Foundation for Distinguished Young Scholar [BK20130034]
- Specialized Research Fund for the Doctoral Program of Higher Education [20123218110009, 20133218110032]
- NUAA Fundamental Research Funds [NE2013105]
- Jiangsu Natural Science Foundation [BK20131298]
- Jiangsu Qinglan Project of China
Error-correcting output coding (ECOC) is one of the most widely used strategies for dealing with multi-class problems by decomposing the original multi-class problem into a series of binary sub-problems. In traditional ECOC-based methods, binary classifiers corresponding to those sub-problems are usually trained separately without considering the relationships among these classifiers. However, as these classifiers are established on the same training data, there may be some inherent relationships among them. Exploiting such relationships can potentially improve the generalization performances of individual classifiers, and, thus, boost ECOC learning algorithms. In this paper, we explore to mine and utilize such relationship through a joint classifier learning method, by integrating the training of binary classifiers and the learning of the relationship among them into a unified objective function. We also develop an efficient alternating optimization algorithm to solve the objective function. To evaluate the proposed method, we perform a series of experiments on eleven datasets from the UCI machine learning repository as well as two datasets from real-world image recognition tasks. The experimental results demonstrate the efficacy of the proposed method, compared with state-of-the-art methods for ECOC-based multi-class classification.
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