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Using multiple classifier behavior to develop a dynamic outlier ensemble
International Journal of Machine Learning and Cybernetics ( IF 3.1 ) Pub Date : 2020-08-09 , DOI: 10.1007/s13042-020-01183-7
Ping Yuan , Biao Wang , Zhizhong Mao

Outlier ensembles that use more base detectors recently become an attractive approach to solving problems of single detectors. However, existing outlier ensembles often assume that base detectors make independent errors, which is difficult to satisfy in practical applications. To this end, this paper proposes a dynamic outlier ensemble to loose this error independence assumption. In our method, it is desired that the most competent base detector(s) can be singled out by the dynamic selection mechanism for each test pattern. The usage of the concept of multiple classifier behavior (MCB) has two purposes. One is to generate artificial outlier examples used for competence estimates. This strategy is different from other methods since we do not make any assumption regarding the data distribution. On the other hand, MCB is used to refine validation sets initialized by the K-nearest neighbors (KNN) rule. It is desired that objects in the refined validation sets are more representative than those found by KNN. With the refined validation sets, competences of all base detectors will be estimated by a probabilistic method, before which we have transformed outputs of base detectors into a probabilistic form. Finally, a switching mechanism that determines whether one detector should be nominated to make the decision or a fusion method should be applied instead is proposed in order to achieve a robust detection result. We carry out experiments on 20 benchmark data sets to verify the effectiveness of our detection method.



中文翻译:

使用多个分类器行为开发动态离群值集合

最近,使用更多基础探测器的离群合奏成为解决单个探测器问题的一种有吸引力的方法。然而,现有的离群合奏通常假定基本检测器产生独立的误差,这在实际应用中很难满足。为此,本文提出了一种动态离群值集合,以松开该错误独立性假设。在我们的方法中,希望可以通过动态选择机制为每个测试图案挑选出最有能力的基本检测器。多重分类器行为(MCB)概念的使用有两个目的。一种是生成用于能力估计的人为异常值示例。此策略与其他方法不同,因为我们不对数据分布进行任何假设。另一方面,MCB用于优化由K最近邻居(KNN)规则初始化的验证集。期望精化的验证集中的对象比KNN发现的对象更具代表性。使用完善的验证集,将通过概率方法估算所有基本检测器的能力,然后将基本检测器的输出转换为概率形式。最后,为了达到鲁棒的检测结果,提出了一种切换机制,该机制确定应指定一个检测器来做出决定,还是应该应用融合方法。我们对20个基准数据集进行了实验,以验证我们检测方法的有效性。期望精化的验证集中的对象比KNN发现的对象更具代表性。使用完善的验证集,将通过概率方法估算所有基本检测器的能力,然后将基本检测器的输出转换为概率形式。最后,为了达到鲁棒的检测结果,提出了一种切换机制,该机制确定应指定一个检测器来做出决定,还是应该应用融合方法。我们对20个基准数据集进行了实验,以验证我们检测方法的有效性。期望精化的验证集中的对象比KNN发现的对象更具代表性。使用完善的验证集,将通过概率方法估算所有基本检测器的能力,然后将基本检测器的输出转换为概率形式。最后,为了达到鲁棒的检测结果,提出了一种切换机制,该机制确定应指定一个检测器来做出决定,还是应该应用融合方法。我们对20个基准数据集进行了实验,以验证我们检测方法的有效性。为了实现鲁棒的检测结果,提出了一种切换机制,该机制确定应指定一个检测器来做出决定还是应该应用融合方法。我们对20个基准数据集进行了实验,以验证我们检测方法的有效性。为了实现鲁棒的检测结果,提出了一种切换机制,该机制确定应指定一个检测器来做出决定还是应该应用融合方法。我们对20个基准数据集进行了实验,以验证我们检测方法的有效性。

更新日期:2020-08-10
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