IGMM-CD: A Gaussian Mixture Classification Algorithm for Data Streams with Concept Drifts
IGMM-CD: A Gaussian Mixture Classification Algorithm for Data Streams with Concept Drifts
复制标题
IGMM-CD：具有概念漂移的数据流高斯混合分类算法
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Gustavo E. A. P. A. Batista
中科院分区：
文献类型：
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作者：
Luan Soares Oliveira;Gustavo E. A. P. A. Batista
从数据流中学习概念与传统的批处理学习有很大的不同，因为在数据流中要学习的概念可能会随着时间的推移而发展。增量学习范式是一种很有前途的数据流学习方法。然而，在存在概念漂移的情况下，过时的概念可能导致错误分类。虽然文献中已经提出了几种增量高斯混合模型方法，但我们注意到这些算法缺乏明确的策略来抛弃过时的概念。本文提出了一种新的基于高斯混合模型的数据流学习增量算法。将该方法与文献中广泛使用的各种算法进行了比较，结果表明该方法在各种场景下与它们竞争，在某些情况下克服了它们。
Learning concepts from data streams differs significantly from traditional batch learning, because in data streams the concepts to be learned may evolve over time. Incremental learning paradigm is a promising approach for learning in a data stream setting. However, in the presence of concept drifts, outdated concepts can cause misclassifications. Although several incremental Gaussian mixture models methods have been proposed in the literature, we notice that these algorithms lack an explicit policy to discard outdated concepts. In this paper, we propose a new incremental algorithm for data stream learning based on Gaussian Mixture Models. The proposed method is compared to various algorithms widely used in the literature, and the results show that it is competitive with them in various scenarios, overcoming them in some cases.