The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it better addresses the bias and variance dilemma than its single-model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining nonstationary data streams can be found in the literature, most of them are crafted under static base-classifier and revisit preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because they involve a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble (pENsemble), is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble's structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams, where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.

中文翻译：

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演化集成模糊分类器


集成学习的概念为复杂环境下的数据流学习提供了一条有前途的途径，因为它比单模型模型更好地解决了偏差和方差的困境，并且具有可重构的结构，非常适合给定的环境。虽然在文献中可以找到用于挖掘非平稳数据流的集成学习的各种扩展，但其中大多数都是在静态基分类器下精心设计的，并在滑动窗口中重新访问先前的样本以进行重新训练步骤。此功能导致计算复杂性过高，并且不够灵活，无法应对快速变化的环境。它们的复杂性通常要求很高，因为由于缺乏结构复杂性降低机制和在线特征选择机制，它们涉及大量离线分类器。本文提出了一种新颖的演化集成分类器，即简约集成（pENsemble）。 pENsemble 与现有架构的不同之处在于，它是基于数据流不断发展的分类器（称为简约分类器）构建的。 pENsemble 配备了集成剪枝机制，可估计基分类器的局部泛化误差。 pENsemble 中集成了动态在线特征选择场景。该方法允许动态选择和取消选择输入特征。 pENsemble 采用动态集成结构来输出最终分类决策，其中它具有新颖的漂移检测场景来增长集成结构。 pENsemble 的功效已通过对动态和不断变化的数据流进行严格的数值研究得到了数值证明，它在实现准确性和复杂性之间的权衡方面提供了最令人鼓舞的性能。