Novel application of heuristic optimisation enables the creation and thorough evaluation of robust support vector machine ensembles for machine learning applications.
Novel application of heuristic optimisation enables the creation and thorough evaluation of robust support vector machine ensembles for machine learning applications.
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DOI：
10.1007/s11306-015-0894-4
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Bessant C
中科院分区：
文献类型：
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作者：
Chatzimichali EA;Bessant C
今天的研究人员可以使用前所未有的强大机器学习工具来构建模型，根据代谢组学特征对样本进行分类（例如将患病样本与健康对照样本分离）。然而，需要谨慎使用这些强大的工具，如果要相信它们的输出，就应该严格评估它们产生的模型的诊断性能。这涉及相当长的处理时间，并且迄今为止需要机器学习方面的专业知识。通过采用受约束的非线性单纯形优化来调整支持向量机（SVM），与传统的网格搜索相比，我们将SVM的训练时间减少了十倍以上，使我们能够实现高性能的R包，使典型的实验室科学家能够在合理的时间尺度内产生功能强大的SVM集成分类器，自动引导训练和严格的排列测试。这将最先进的开源多变量分类管道交到每个代谢组学研究人员手中，使他们能够构建具有现实性能指标的强大分类模型。
Today’s researchers have access to an unprecedented range of powerful machine learning tools with which to build models for classifying samples according to their metabolomic profile (e.g. separating diseased samples from healthy controls). However, such powerful tools need to be used with caution and the diagnostic performance of models produced by them should be rigorously evaluated if their output is to be believed. This involves considerable processing time, and has hitherto required expert knowledge in machine learning. By adopting a constrained nonlinear simplex optimisation for the tuning of support vector machines (SVMs) we have reduced SVM training times more than tenfold compared to a traditional grid search, allowing us to implement a high performance R package that makes it possible for a typical bench scientist to produce powerful SVM ensemble classifiers within a reasonable timescale, with automated bootstrapped training and rigorous permutation testing. This puts a state-of-the-art open source multivariate classification pipeline into the hands of every metabolomics researcher, allowing them to build robust classification models with realistic performance metrics.