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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Entropic One-Class Classifiers
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Entropic One-Class Classifiers

机译：熵一类分类器

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摘要

The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patterns are termed nontarget, and therefore, they should be recognized as such. In this paper, we propose a novel one-class classification system that is based on an interplay of different techniques. Primarily, we follow a dissimilarity representation-based approach; we embed the input data into the dissimilarity space (DS) by means of an appropriate parametric dissimilarity measure. This step allows us to process virtually any type of data. The dissimilarity vectors are then represented by weighted Euclidean graphs, which we use to determine the entropy of the data distribution in the DS and at the same time to derive effective decision regions that are modeled as clusters of vertices. Since the dissimilarity measure for the input data is parametric, we optimize its parameters by means of a global optimization scheme, which considers both mesoscopic and structural characteristics of the data represented through the graphs. The proposed one-class classifier is designed to provide both hard (Boolean) and soft decisions about the recognition of test patterns, allowing an accurate description of the classification process. We evaluate the performance of the system on different benchmarking data sets, containing either feature-based or structured patterns. Experimental results demonstrate the effectiveness of the proposed technique.
机译：一类分类问题是模式识别中的众所周知的研究努力。该问题也以不同的名称（例如异常值和新颖性/异常检测）而为人所知。问题的核心在于建模和识别仅属于所谓目标类的模式。所有其他模式都称为非目标模式，因此，应将其识别为非目标模式。在本文中，我们提出了一种基于不同技术的相互作用的新颖的一类分类系统。首先，我们采用基于差异表示的方法；我们通过适当的参数化差异度量将输入数据嵌入到差异空间（DS）中。此步骤使我们能够处理几乎任何类型的数据。然后用加权的欧几里得图表示不相似向量，我们用它们来确定DS中数据分布的熵，并同时导出建模为顶点簇的有效决策区域。由于输入数据的相异性度量是参数化的，因此我们通过全局优化方案来优化其参数，该方案同时考虑了通过图表表示的数据的介观和结构特征。提出的一类分类器旨在提供有关测试模式识别的硬（布尔）和软决策，从而可以对分类过程进行准确的描述。我们在包含基于特征或结构化模式的不同基准数据集上评估系统的性能。实验结果证明了该技术的有效性。

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