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首页> 外文期刊>Expert Systems with Application >Hierarchical co-evolutionary clustering tree-based rough feature game equilibrium selection and its application in neonatal cerebral cortex MRI
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Hierarchical co-evolutionary clustering tree-based rough feature game equilibrium selection and its application in neonatal cerebral cortex MRI

机译：基于层次共进化聚类树的粗糙特征博弈均衡选择及其在新生儿大脑皮层MRI中的应用

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摘要

A wide variety of feature selection methods have been developed as promising solutions to find the classification pattern inside increasing applications. But the exploring efficient, flexible and robust feature selection method to handle the rising big data is still an exciting challenge. This paper presents a novel hierarchical co-evolutionary clustering tree-based rough feature game equilibrium selection algorithm (CTFGES). It aims to select out the high-quality feature subsets, which can enrich the research of feature selection and classification in the heterogeneous big data. Firstly, we construct a flexible hierarchical co-evolutionary clustering tree model to speed up the process of feature selection, which can effectively extract the features from the parent and children branches of four-layer co-evolutionary clustering tree. Secondly, we design a mixed co-evolutionary game equilibrium scheme with adaptive dynamics to guide parent and children branch subtrees to approach the optimal equilibrium regions, and enable their feature sets to converge stably to the Nash equilibrium. So both noisy heterogeneous features and non-identified redundant ones can be further eliminated. Finally, the extensive experiments on various big datasets are conducted to demonstrate the more excellent performance of CTFGES, in terms of accuracy, efficiency and robustness, compared with the representative feature selection algorithms. In addition, the proposed CTFGES algorithm has been successfully applied into the feature segmentation of large-scale neonatal cerebral cortex MRI with varying noise ratios and intensity non-uniformity levels. The results indicate that it can be adaptive to derive from the cortical folding surfaces and achieves the satisfying consistency with medical experts, which will be potential significance for successfully assessing the impact of aberrant brain growth on the neurodevelopment of neonatal cerebrum. (C) 2018 Elsevier Ltd. All rights reserved.
机译：已经开发了各种各样的特征选择方法作为有前途的解决方案，以在不断增加的应用程序中找到分类模式。但是，探索有效，灵活和健壮的特征选择方法来处理不断增长的大数据仍然是一个令人兴奋的挑战。本文提出了一种新颖的基于层次共进化树的粗糙特征博弈均衡选择算法（CTFGES）。其目的是选择高质量的特征子集，以丰富异构大数据中特征选择和分类的研究。首先，我们构建了一个灵活的分层协同进化聚类树模型，以加快特征选择的过程，可以有效地从四层协同进化聚类树的父子分支中提取特征。其次，我们设计了具有自适应动力学的混合协同进化博弈均衡方案，以指导父子分支树接近最佳均衡区域，并使它们的特征集稳定地收敛到纳什均衡。因此，可以进一步消除嘈杂的异构特征和未标识的冗余特征。最后，在各种大型数据集上进行了广泛的实验，以证明与代表性特征选择算法相比，CTFGES在准确性，效率和鲁棒性方面具有更出色的性能。此外，所提出的CTFGES算法已成功应用于具有变化的噪声比和强度不均匀度的大规模新生儿大脑皮层MRI的特征分割。结果表明，它可以适应皮层折叠表面并与医学专家取得令人满意的一致性，这对于成功评估异常大脑生长对新生儿大脑神经发育的影响具有潜在的意义。 （C）2018 Elsevier Ltd.保留所有权利。

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