分层多标签交叉验证的新分裂质量度量及其在大型稀疏基因本体数据集上的应用
Novel split quality measures for stratified multilabel Cross Validation with application to large and sparse gene ontology datasets
摘要：
多标记学习是机器学习研究中的一个重要课题。在多标签设置中评估模型需要为多标签数据设计的特定交叉验证方法。在这篇文章中，我们证明了最广泛使用的交叉验证分割质量度量在具有强烈类别不平衡的多标签数据中表现不佳。我们提出了改进的措施和算法，optisplit，优化交叉验证分裂。我们提出了一个广泛的比较各种类型的交叉验证方法，其中我们表明，optisplit产生更多的交叉验证分裂比现有的方法，它是足够快的大基因本体（GO）数据集上使用。
英文摘要：
Multilabel learning is an important topic in machine learning research. Evaluating models in multilabel settings requires specific cross validation methods designed for multilabel data. In this article, we show that the most widely used cross validation split quality measures do not behave adequately with multilabel data that has strong class imbalance. We present improved measures and an algorithm, optisplit, for optimising cross validations splits. We present an extensive comparison of various types of cross validation methods in which we show that optisplit produces more even cross validation splits than the existing methods and that it is fast enough to be used on big Gene Ontology (GO) datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
