One-Class Classification with Extreme Learning Machine
One-Class Classification with Extreme Learning Machine
复制标题
使用极限学习机进行一类分类
DOI：
10.1155/2015/412957
复制
发表时间：
2015-01-01
影响因子：
--
通讯作者：
Su, Guiping
中科院分区：
文献类型：
--
作者：
Leng, Qian;Qi, Honggang;Su, Guiping
一类分类问题在过去的几十年里得到了深入的研究。在一类分类中最有效的神经网络方法之一，自动编码器已经被成功地应用于许多应用。然而，这种分类器依赖于传统的学习算法如反向传播来训练网络，这是相当耗时的。针对自动编码神经网络学习速度慢的问题，提出了一种简单高效的基于极限学习机的一类分类器。ELM的本质是不需要调整隐含层，输出权值可以解析确定，从而大大加快了学习速度。在几个真实的基准测试上进行的实验评估表明，基于ELM的单类分类器的学习速度比自动编码器快数百倍，并且与各种单类分类方法相比具有竞争力。
One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.