EvoSplit：一种将多标签数据集分割为不相交子集的进化方法
EvoSplit: An evolutionary approach to split a multi-label data set into disjoint subsets
摘要：
本文提出了一种新的进化方法，EvoSplit，用于将多标签数据集分配到不相交的子集中，以进行监督机器学习。目前，数据集提供者要么随机划分数据集，要么使用迭代分层，这种方法旨在将原始数据集的标签（或标签对）分布保持在不同的子集中。遵循同样的目标，本文首先介绍了一个单目标的进化方法，试图获得一个分裂，最大限度地提高这些分布之间的相似性独立。其次，提出了一种新的多目标进化算法，同时考虑两个分布（标签和标签对）的相似性最大化。这两种方法都使用众所周知的多标签数据集以及目前在计算机视觉和机器学习应用中使用的大型图像数据集进行了验证。EvoSplit改进了数据集的分裂，与迭代分层相比，遵循不同的措施：标签分布，标签对分布，示例分布，折叠和折叠标签对零阳性示例。
英文摘要：
This paper presents a new evolutionary approach, EvoSplit, for the distribution of multi-label data sets into disjoint subsets for supervised machine learning. Currently, data set providers either divide a data set randomly or using iterative stratification, a method that aims to maintain the label (or label pair) distribution of the original data set into the different subsets. Following the same aim, this paper first introduces a single-objective evolutionary approach that tries to obtain a split that maximizes the similarity between those distributions independently. Second, a new multi-objective evolutionary algorithm is presented to maximize the similarity considering simultaneously both distributions (labels and label pairs). Both approaches are validated using well-known multi-label data sets as well as large image data sets currently used in computer vision and machine learning applications. EvoSplit improves the splitting of a data set in comparison to the iterative stratification following different measures: Label Distribution, Label Pair Distribution, Examples Distribution, folds and fold-label pairs with zero positive examples.
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。
