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Dynamically adaptive and diverse dual ensemble learning approach for handling concept drift in data streams
Computational Intelligence ( IF 1.8 ) Pub Date : 2021-07-15 , DOI: 10.1111/coin.12475
Kanu Goel 1 , Shalini Batra 1
Affiliation  

Concept drift refers to the change in data distributions and evolving relationships between input and output variables with the passage of time. To analyze such variations in learning environments and generate models which can accommodate changing performance of predictive systems is one of the challenging machine learning applications. In general, the majority of the existing schemes consider one of the specific drift types: gradual, abrupt, recurring, or mixed, with traditional voting setup. In this work, we propose a novel data stream framework, dynamically adaptive and diverse dual ensemble (DA-DDE) which responds to multiple drift types in the incoming data streams by combining online and block-based ensemble techniques. In the proposed scheme, a dual diversified ensemble-based system is constructed with the combination of active and passive ensembles, updated over a diverse set of resampled input space. The adaptive weight setting method is proposed in this work which utilizes the overall performance of learners on historic as well as recent concepts of distributions. Further a dual voting system has been used for hypothesis generation by considering dynamic adaptive credibility of ensembles in real time. Comparative analysis with 14 state-of-the-art algorithms on 24 artificial and 11 real datasets shows that DA-DDE is highly effective in handling various drift types.

中文翻译:

用于处理数据流中概念漂移的动态自适应和多样化双重集成学习方法

概念漂移是指数据分布的变化以及随着时间的推移输入和输出变量之间不断变化的关系。分析学习环境中的这种变化并生成能够适应预测系统不断变化的性能的模型是具有挑战性的机器学习应用之一。通常,大多数现有方案都考虑了一种特定的漂移类型:渐进式、突然式、重复式或混合式,以及传统的投票设置。在这项工作中,我们提出了一种新颖的数据流框架,即动态自适应和多样化双重集成(DA-DDE),它通过结合在线和基于块的集成技术来响应传入数据流中的多种漂移类型。在提议的方案中,结合主动和被动集成构建了一个基于双重多样化集成的系统,并在一组不同的重采样输入空间上进行了更新。在这项工作中提出了自适应权重设置方法,该方法利用了学习者在历史和近期分布概念上的整体表现。此外,通过实时考虑集成的动态自适应可信度,已将双重投票系统用于假设生成。在 24 个人工数据集和 11 个真实数据集上与 14 个最先进的算法进行比较分析表明,DA-DDE 在处理各种漂移类型方面非常有效。在这项工作中提出了自适应权重设置方法,该方法利用了学习者在历史和近期分布概念上的整体表现。此外,通过实时考虑集成的动态自适应可信度,已将双重投票系统用于假设生成。在 24 个人工数据集和 11 个真实数据集上与 14 个最先进的算法进行比较分析表明,DA-DDE 在处理各种漂移类型方面非常有效。在这项工作中提出了自适应权重设置方法,该方法利用了学习者在历史和近期分布概念上的整体表现。此外,通过实时考虑集成的动态自适应可信度,已将双重投票系统用于假设生成。在 24 个人工数据集和 11 个真实数据集上与 14 个最先进的算法进行比较分析表明,DA-DDE 在处理各种漂移类型方面非常有效。
更新日期:2021-07-15
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