SFNClassifier:一个无标度的社会网络方法来处理概念漂移

J. P. Barddal, Heitor Murilo Gomes, F. Enembreck
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引用次数: 19

摘要

在本文中,我们提出了一种新的集成方法,即无标度网络分类器(SFNClassifier),它被认为是一个动态大小的无标度网络。在数据流挖掘中,提出了基于集成的方法来提高准确性并允许从概念漂移中快速恢复。然而,这些方法都是基于更新和轮询启发式,不能在任意领域提供良好的准确性结果,也不能明确表示分类器之间的相似性。作为网络的集成表示允许我们提取中心性度量,这些度量用于执行加权多数投票,其中分类器的权重与其中心性值成比例。基于实证研究,我们得出结论,SFNClassifier在准确率方面与其他集成学习器具有可比性,并且在处理时间上优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SFNClassifier: a scale-free social network method to handle concept drift
In this paper, we present a new ensemble method, the Scale-free Network Classifier (SFNClassifier), that is conceived as a dynamic sized scale-free network. In Data Stream Mining, ensemble-based approaches have been proposed to enhance accuracy and allow fast recovery from concept drift. However, these approaches are based on both update and polling heuristics that do not present good accuracy results in arbitrary domains and do not represent explicitly the similarity between classifiers. The representation of the ensemble as a network allows us to extract centrality metrics, which are used to perform a weighted majority vote, where the weight of a classifier is proportional to its centrality value. Based on empirical studies, we concluded that SFNClassifier has comparable results to other ensemble-learners in terms of accuracy and outperformed the other methods in processing time.
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