Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer coevolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility.

中文翻译：

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基于多层协同进化共识MapReduce的多相关特征集成选择


尽管大数据的特征选择已在数据挖掘、机器学习和模式识别领域得到深入研究，但挑战不仅在于发明新算法来处理应用中的噪声和不确定大数据，还在于链接多个相关特征源，结构化或非结构化，以开发有效的特征缩减方法。在本文中，我们提出了一种基于多层协同进化共识MapReduce（MCCM）的多重相关特征集成选择（MRFES）算法。我们构建了一个有效的 MCCM 模型来处理具有多个相关特征源的大规模数据集的特征集成选择，并探索通过参与协作特征的共同进化 memeplex 实现的局部解决方案和全局优势解决方案之间的统一一致性聚合乐团选择过程。该模型试图在共同进化模因复合体之间达成共同决策协议，这要求需要机制来检测一些非合作共同进化行为并实现更好的纳什均衡解决方案。大量的实验比较研究证实了 MRFES 在一些知名基准数据集上解决具有复杂噪声和多个相关特征源的大规模数据集问题的有效性。该算法可以极大地促进从原始特征空间中选择相关特征子集，并具有更好的准确性、效率和可解释性。此外，我们将 MRFES 应用于基于人类大脑皮层的分类预测。这些成功的应用预计将在效率和可行性方面显着扩大大规模和复杂大脑数据的分类预测。