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首页> 外文会议>Proceedings of the Twenty-Third international joint conference on artificial intelligence >Graph Classification with Imbalanced Class Distributions and Noise
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Graph Classification with Imbalanced Class Distributions and Noise

机译：具有不平衡类分布和噪声的图分类

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Recent years have witnessed an increasing number of applications involving data with structural dependency and graph representations.For these applications,it is very common that their class distribution is imbalanced with minority samples being only a small portion of the population.Such imbalanced class distributions impose significant challenges to the learning algorithms.This problem is further complicated with the presence of noise or outliers in the graph data.In this paper,we propose an imbalanced graph boosting algorithm,igBoost,that progressively selects informative subgraph patterns from imbalanced graph data for learning.To handle class imbalance,we take class distributions into consideration to assign different weight values to graphs.The distance of each graph to its class center is also considered to adjust the weight to reduce the impact of noisy graph data.The weight values are integrated into the iterative subgraph feature selection and margin learning process to achieve maximum benefits.Experiments on realworld graph data with different degrees of class imbalance and noise demonstrate the algorithm performance.
机译：近年来，越来越多的应用程序涉及具有结构相关性和图形表示的数据。对于这些应用程序，其类分布不平衡且少数样本仅占人口的一小部分是非常普遍的。图数据中存在噪声或离群值，使该问题进一步复杂化。本文提出了一种不平衡图提升算法igBoost，该算法从不平衡图数据中逐步选择信息性子图模式进行学习。为了解决类别不平衡问题，我们考虑类别分布，为图表分配不同的权重值。还考虑了每个图表与类别中心的距离，以调整权重，以减少嘈杂的图表数据的影响。迭代子图特征选择和边缘学习过程的实现具有最大的收益。在具有不同程度的类不平衡和噪声的现实世界图形数据上的实验证明了该算法的性能。

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