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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
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Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm

机译：应对不同类型的概念漂移：精度更新的集成算法

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摘要

Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges in learning from data streams is reacting to concept drift, i.e., unforeseen changes of the stream's underlying data distribution. Several classification algorithms that cope with concept drift have been put forward, however, most of them specialize in one type of change. In this paper, we propose a new data stream classifier, called the Accuracy Updated Ensemble (AUE2), which aims at reacting equally well to different types of drift. AUE2 combines accuracy-based weighting mechanisms known from block-based ensembles with the incremental nature of Hoeffding Trees. The proposed algorithm is experimentally compared with 11 state-of-the-art stream methods, including single classifiers, block-based and online ensembles, and hybrid approaches in different drift scenarios. Out of all the compared algorithms, AUE2 provided best average classification accuracy while proving to be less memory consuming than other ensemble approaches. Experimental results show that AUE2 can be considered suitable for scenarios, involving many types of drift as well as static environments.
机译：由于数据流挖掘在传感器网络，银行业务和电信等广泛的应用中的存在，已引起越来越多的关注。从数据流中学习的最重要挑战之一是对概念漂移（即流的基础数据分布的不可预见的变化）做出反应。已经提出了几种解决概念漂移的分类算法，但是，大多数算法专门针对一种类型的变化。在本文中，我们提出了一种新的数据流分类器，称为“精确度更新的合奏”（AUE2），其目的是对不同类型的漂移具有同等的反应。 AUE2将基于块的集成已知的基于精度的加权机制与Hoeffding树的增量性质相结合。实验上将所提出的算法与11种最新流方法进行了比较，这些方法包括单个分类器，基于块的集成和在线集成以及在不同漂移情况下的混合方法。在所有比较的算法中，AUE2提供了最佳的平均分类准确性，同时被证明比其他集成方法更少的内存消耗。实验结果表明，AUE2可以被认为适用于涉及多种类型的漂移以及静态环境的场景。

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