In the paper, we propose two gene expression programming (GEP)-based ensemble classifiers with different drift detection mechanisms. In the related work section, we briefly review GEP as a classification tool, incremental classifiers, and concept drift detectors. Next, the structure of our two-level GEP ensemble with metagenes is described. Further on, two integrated classifiers with drift detection algorithm and Wilcoxon rank sum test drift detector are proposed. The approach is validated in the computational experiment in which several real-life and artificial datasets with concept drift have been used. Experiment confirmed that the proposed approach can be competitive to existing solutions. In the conclusion section, we briefly outline directions for future research.

中文翻译：

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具有漂移检测功能的基于 GEP 的分类器

在本文中，我们提出了两种具有不同漂移检测机制的基于基因表达编程（GEP）的集成分类器。在相关工作部分，我们简要回顾了 GEP 作为分类工具、增量分类器和概念漂移检测器。接下来，描述了我们带有元基因的两级 GEP 集成的结构。进一步，提出了具有漂移检测算法和Wilcoxon秩和测试漂移检测器的两个集成分类器。该方法在计算实验中得到了验证，其中使用了几个具有概念漂移的现实生活和人工数据集。实验证实，所提出的方法可以与现有解决方案竞争。在结论部分，我们简要概述了未来研究的方向。