Extreme learning machine for multi-categories classification applications
Extreme learning machine for multi-categories classification applications
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DOI：
10.1109/ijcnn.2008.4634028
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发表时间：
2008-06
期刊：
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通讯作者：
Hai-Jun Rong;G. Huang;Y. Ong
中科院分区：
文献类型：
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作者：
Hai-Jun Rong;G. Huang;Y. Ong
本文研究了基于极限学习机的多类模式分类问题。该研究基于一系列ELM二进制分类器或单个ELM分类器。当使用二进制ELM分类器时，使用一对一(OAA)和一对一(OAO)方案将多类问题分解为两类问题，为简洁起见，分别称为ELM-OAA和ELM-OAO。在单个ELM分类器中，多类问题是用等于模式类数的多输出节点的结构来实现的。使用多类基准问题对它们的性能进行了评估，仿真结果表明ELM-OAA和ELM-OAO比单个ELM分类器需要更少的隐藏节点。此外，当模式类标签不大于10时，ELM-OAO的计算量通常与单个ELM分类器相当或更小。
In the paper, the multi-class pattern classification using extreme learning machine (ELM) is studied. The study is based on either a series of ELM binary classifiers or a single ELM classifier. When using binary ELM classifiers, the multi-class problem is decomposed into two-class problem using the one-against-all (OAA) and one-against-one (OAO) schemes, which are named as ELM-OAA and ELM-OAO respectively for brevity. In a single ELM classifier, the multi-class problem is implemented with an architecture of multi-output nodes which is equal to the number of pattern classes. Their performance is evaluated using some multi-class benchmark problems and simulation results show that ELM-OAA and ELM-OAO requires fewer hidden nodes than the single ELM classifier. In addition ELM-OAO usually has similar or less computation burden than the single ELM classifier when the pattern class labels is not larger than 10.