论文标题
基于区域漂移分歧的概念漂移适应的各种实例加权合奏
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation
论文作者
论文摘要
概念漂移是指基础数据分布的变化，并且是不断发展的数据流的固有属性。通过动态分类器的合奏学习被证明是处理概念漂移的有效方法。但是，通过不断发展的流创建和维持合奏多样性的最佳方法仍然是一个具有挑战性的问题。与通过输入，输出或分类器参数估算多样性相反，我们根据合奏成员是否同意区域分布变化的概率提出多样性测量。在我们的方法中，对区域分布变化的估计用作实例权重。通过不同的方案构建不同的区域集将导致不同的漂移估计结果，从而创造多样性。选择最不同意的分类器是为了最大程度地提高多样性。因此，开发了一种基于实例的集合学习算法，称为不同实例加权集合（DIWE），以解决数据流分类问题的概念漂移。对各种合成和现实数据流基准的评估显示了所提出算法的有效性和优势。
Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved to be an efficient method of handling concept drift. However, the best way to create and maintain ensemble diversity with evolving streams is still a challenging problem. In contrast to estimating diversity via inputs, outputs, or classifier parameters, we propose a diversity measurement based on whether the ensemble members agree on the probability of a regional distribution change. In our method, estimations over regional distribution changes are used as instance weights. Constructing different region sets through different schemes will lead to different drift estimation results, thereby creating diversity. The classifiers that disagree the most are selected to maximize diversity. Accordingly, an instance-based ensemble learning algorithm, called the diverse instance weighting ensemble (DiwE), is developed to address concept drift for data stream classification problems. Evaluations of various synthetic and real-world data stream benchmarks show the effectiveness and advantages of the proposed algorithm.
