在线和离线单类分类的极限学习机的构建-扩展的机器学习
On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox
摘要：
单类分类（OCC）一直是研究人员关注的主要问题，并有效地应用于各个学科。但是，传统的基于单类分类器的方法由于其迭代过程和各种参数的调整而非常耗时。本文提出了六种基于极限学习机（ELM）和在线顺序ELM（OSELM）的OCC方法。我们提出的分类器主要分为两类：基于重建的分类器和基于边界的分类器，它们支持两种类型的学习，即，在线和离线学习。在提出的各种方法中，四种是离线方法，其余两种是在线方法。在四种离线方法中，两种方法执行随机特征映射，两种方法执行内核特征映射。基于核特征映射的方法已经用RBF核进行了测试，在线版本的单类分类器用两种类型的节点进行了测试，添加剂和RBF。众所周知，在OCC的情况下，阈值确定是一个关键因素，因此，到目前为止，已经采用了三种不同的阈值确定标准，并分析了一种阈值确定标准相对于另一种阈值确定标准的有效性。此外，这些方法在两个人工数据集上进行测试，以检查其边界构建能力，并在来自不同学科的八个基准数据集上进行测试，以评估分类器的性能。我们提出的分类器表现出更好的性能相比，十个传统的一类分类器和基于ELM的两个一类分类器。通过提出的一类分类器，我们打算扩大OCC最常用的工具箱，即DD工具箱的功能。我们所有的方法都与工具箱的所有现有功能完全兼容。
英文摘要：
One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods based on extreme learning machine (ELM) and Online Sequential ELM (OSELM). Our proposed classifiers mainly lie in two categories: reconstruction based and boundary based, which supports both types of learning viz., online and offline learning. Out of various proposed methods, four are offline and remaining two are online methods. Out of four offline methods, two methods perform random feature mapping and two methods perform kernel feature mapping. Kernel feature mapping based approaches have been tested with RBF kernel and online version of one-class classifiers are tested with both types of nodes viz., additive and RBF. It is well known fact that threshold decision is a crucial factor in case of OCC, so, three different threshold deciding criteria have been employed so far and analyses the effectiveness of one threshold deciding criteria over another. Further, these methods are tested on two artificial datasets to check there boundary construction capability and on eight benchmark datasets from different discipline to evaluate the performance of the classifiers. Our proposed classifiers exhibit better performance compared to ten traditional one-class classifiers and ELM based two one-class classifiers. Through proposed one-class classifiers, we intend to expand the functionality of the most used toolbox for OCC i.e. DD toolbox. All of our methods are totally compatible with all the present features of the toolbox.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
