Speeding Up Recovery from Concept Drifts
Speeding Up Recovery from Concept Drifts
复制标题
加速从概念漂移中恢复
DOI：
10.1007/978-3-662-44845-8_12
复制
发表时间：
2014
期刊：
影响因子：
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通讯作者：
Roberto S. M. Barros
中科院分区：
文献类型：
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作者：
S. G. T. C. Santos;Paulo Mauricio Gonçalves Júnior;Geyson Daniel dos Santos Silva;Roberto S. M. Barros
从数据流中提取知识是一项需求不断增加的活动。然而，在这种类型的环境中，数据分布的变化或概念漂移可能会不断发生，这是一个挑战。提出了一种改进的基于适应多样性的在线提升算法ADOB（AdaptableDiversity-basedOnlineBoosting），旨在加快概念漂移后的专家恢复速度。我们进行了实验，以比较ADOB的准确性以及执行时间和内存使用与其他一些方法使用几个人工和真实世界的数据集，从该地区最常用的。结果表明，在许多不同的情况下，所提出的方法保持了很高的准确性，优于其他测试方法的规律性，在执行时间和内存使用没有显着变化。特别是，ADOB是特别有效的情况下，频繁和突然的概念漂移发生。
The extraction of knowledge from data streams is an activity that has progressively been receiving an increased demand. However, in this type of environment, changes in data distribution, or concept drift, can occur constantly and is a challenge. This paper proposes theAdaptable Diversity-based Online Boosting (ADOB), a modified version of the online boosting, as proposed by Oza and Russell, which is aimed at speeding up the experts recovery after concept drifts. We performed experiments to compare the accuracy as well as the execution time and memory use of ADOB against a number of other methods using several artificial and real-world datasets, chosen from the most used ones in the area. Results suggest that, in many different situations, the proposed approach maintains a high accuracy, outperforming the other tested methods in regularity, with no significant change in the execution time and memory use. In particular, ADOB was specially efficient in situations where frequent and abrupt concept drifts occur.