In recent years, the prevalence of technological advances has led to an enormous and ever-increasing amount of data that are now commonly available in a streaming fashion. In such nonstationary environments, the underlying process generating the data stream is characterized by an intrinsic nonstationary or evolving or drifting phenomenon known as concept drift. Given the increasingly common applications whose data generation mechanisms are susceptible to change, the need for effective and efficient algorithms for learning from and adapting to evolving or drifting environments can hardly be overstated. In dynamic environments associated with concept drift, learning models are frequently updated to adapt to changes in the underlying probability distribution of the data. A lot of work in the area of learning in nonstationary environments focuses on updating the learning predictive model to optimize recovery from concept drift and convergence to new concepts by adjusting parameters and discarding poorly performing models while little effort has been dedicated to investigate what type of learning model is suitable at any given time for different types of concept drift. In this paper, we investigate the impact of heterogeneous online ensemble learning based on online model selection for predictive modeling in dynamic environments. We propose a novel heterogeneous ensemble approach based on online dynamic ensemble selection that accurately interchanges between different types of base models in an ensemble to enhance its predictive performance in nonstationary environments. The approach is known as Heterogeneous Dynamic Ensemble Selection based on Accuracy and Diversity (HDES-AD) and makes use of models generated by different base learners to increase diversity to circumvent problems associated with existing dynamic ensemble classifiers that may experience loss of diversity due to the exclusion of base learners generated by different base algorithms. The algorithm is evaluated on artificial and real-world datasets with well-known online homogeneous online ensemble approaches such as DDD, AFWE, and OAUE. The results show that HDES-AD performed significantly better than the other three homogeneous online ensemble approaches in nonstationary environments.

中文翻译：

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非平稳环境的自适应异构在线学习集合分类器

近年来，技术进步的盛行导致海量且不断增加的数据，这些数据现在通常以流方式获得。在这样的非平稳环境中，生成数据流的基础过程的特征是固有的非平稳或演化或漂移现象，称为概念漂移。鉴于其数据生成机制容易发生变化的越来越普遍的应用程序，对于从中学习或适应不断变化或不断变化的环境的高效算法的需求几乎不会被夸大。在与概念漂移相关的动态环境中，学习模型会经常更新以适应数据潜在概率分布的变化。非平稳环境中学习领域的许多工作都集中在更新学习预测模型上，以通过调整参数并丢弃性能不佳的模型来优化从概念漂移和收敛到新概念的恢复，而很少投入精力来研究哪种类型的学习该模型在任何给定时间都适用于不同类型的概念漂移。在本文中，我们研究了基于在线模型选择的异构在线集成学习对动态环境中的预测建模的影响。我们提出了一种基于在线动态集成选择的新颖的异构集成方法，该方法可在集成中的不同类型的基本模型之间准确互换，以增强其在非平稳环境中的预测性能。该方法称为基于准确性和多样性的异构动态集合选择（HDES-AD），它利用不同基础学习者生成的模型来增加多样性，从而规避与现有动态集成分类器相关的问题，这些问题可能会由于排除由不同基础算法生成的基础学习者。使用众所周知的在线同类在线集成方法（例如DDD，AFWE和OAUE）在人工和现实数据集上评估该算法。结果表明，在非平稳环境中，HDES-AD的性能明显优于其他三种同类在线集成方法。