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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Concept Drift Detection for Graph-Structured Classifiers under Scarcity of True Labels
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Concept Drift Detection for Graph-Structured Classifiers under Scarcity of True Labels

机译：缺少真标签时图结构分类器的概念漂移检测

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摘要

Data stream classifiers that can withstand unusual phenomena in an evolving data stream, such as concept drift and concept evolution, are highly desirable for data stream mining. Most existing methods deal with such phenomena in a supervised manner, which is costly in a real-world scenario. To address this shortcoming, we propose a concept drift detection approach that combines our approach with a semi-supervised adaptive incremental neural gas (A2ING) classifier. Our approach makes use of A2ING's graph topology structure to detect changes in a data stream. We derive a graph's instability around its decision boundary and find the difference in prior and posterior distributions of the criteria. The empirical results show the effectiveness of our method. The classifier requires a relatively low number of true labels compared to existing approaches and shows high effectiveness in change detection.
机译：对于数据流挖掘，非常需要能够经受住不断发展的数据流中异常现象（例如概念漂移和概念演化）的数据流分类器。大多数现有方法都以监督方式处理此类现象，这在现实世界中是昂贵的。为了解决此缺点，我们提出了一种概念漂移检测方法，该方法将我们的方法与半监督的自适应增量神经气体（A2ING）分类器相结合。我们的方法利用A2ING的图拓扑结构来检测数据流中的变化。我们推导图的决策边界附近的不稳定性，并找到标准的前后分布的差异。实验结果表明了该方法的有效性。与现有方法相比，该分类器所需的真实标签数量相对较少，并且在更改检测中显示出很高的有效性。

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