Abstract

Detecting fall compensatory behaviour from large EEG datasets poses a difficult problem in big data which can be alleviated by evolutionary computation-based machine learning strategies. In this article, hyperheuristic optimisation solutions via evolutionary optimisation of deep neural network topologies and genetic programming of machine learning pipelines will be investigated. Wavelet extractions from signals recorded during physical activities present a binary problem for detecting fall compensation. The earlier results show that a Gaussian process model achieves an accuracy of 86.48%. Following this, artificial neural networks are evolved through evolutionary algorithms and score similarly to most standard models; the hyperparameters chosen are well outside the bounds of batch or manual searches. Five iterations of genetic programming scored higher than all other approaches, at a mean 90.52% accuracy. The best pipeline extracted polynomial features and performed Principal Components Analysis, before machine learning through a randomised set of decision trees, and passing the class prediction probabilities to a 72-nearest-neighbour algorithm. The best genetic solution could infer data in 0.02 s, whereas the second best genetic programming solution (89.79%) could infer data in only 0.3 ms.

Graphical abstract

中文翻译：

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使用神经进化和遗传超参数优化的 EEG 跌倒补偿检测

摘要

从大型 EEG 数据集中检测跌倒补偿行为是大数据中的一个难题，可以通过基于进化计算的机器学习策略来缓解这一难题。在本文中，将研究通过深度神经网络拓扑的进化优化和机器学习管道的遗传编程的超启发式优化解决方案。从身体活动期间记录的信号中提取小波提出了检测跌倒补偿的二元问题。较早的结果表明，高斯过程模型达到了 86.48% 的准确率。在此之后，人工神经网络通过进化算法进化，并获得与大多数标准模型相似的分数；选择的超参数远远超出了批量或手动搜索的范围。遗传编程的五次迭代得分高于所有其他方法，平均准确率为 90.52%。最好的管道提取多项式特征并执行主成分分析，然后通过一组随机决策树进行机器学习，并将类别预测概率传递给 72 最近邻算法。最好的遗传解决方案可以在 0.02 秒内推断出数据，而第二好的遗传编程解决方案 (89.79%) 只能在 0.3 毫秒内推断出数据。

图形概要