论文标题
一级分类
Minimum Variance Embedded Auto-associative Kernel Extreme Learning Machine for One-class Classification
论文作者
论文摘要
一级分类（OCC）仅需要一个类别的样本来训练分类器。最近，为OCC任务开发了一台自动促进内核极限学习机。本文通过将最小差异信息嵌入其架构中，引入了该分类器的新扩展，并被称为Vaakelm。最小差异嵌入迫使网络输出权重集中在低方差区域，并减少阶层内差异。这导致了目标样本和离群值的更好分离，从而改善了分类器的概括性能。拟议的分类器遵循一种基于重建的方法，通过使用内核极限学习机作为基本分类器，将重建误差最小化，并最大程度地减少重建误差。它使用重建误差中的偏差来识别异常值。我们对15个小规模和10个中型一级基准数据集进行实验，以证明所提出的分类器的效率。我们通过将平均F1分数视为比较度量标准，将结果与13个现有的一级分类器进行了比较。实验结果表明，Vaakelm的性能始终比现有分类器更好，这使其成为OCC任务的可行替代方案。
One-class classification (OCC) needs samples from only a single class to train the classifier. Recently, an auto-associative kernel extreme learning machine was developed for the OCC task. This paper introduces a novel extension of this classifier by embedding minimum variance information within its architecture and is referred to as VAAKELM. The minimum variance embedding forces the network output weights to focus in regions of low variance and reduces the intra-class variance. This leads to a better separation of target samples and outliers, resulting in an improvement in the generalization performance of the classifier. The proposed classifier follows a reconstruction-based approach to OCC and minimizes the reconstruction error by using the kernel extreme learning machine as the base classifier. It uses the deviation in reconstruction error to identify the outliers. We perform experiments on 15 small-size and 10 medium-size one-class benchmark datasets to demonstrate the efficiency of the proposed classifier. We compare the results with 13 existing one-class classifiers by considering the mean F1 score as the comparison metric. The experimental results show that VAAKELM consistently performs better than the existing classifiers, making it a viable alternative for the OCC task.
