Automated Machine Learning for Multi-Label Classification

arXiv:2402.18198v1 Announce Type: new
Abstract: Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For supervised learning tasks, most notably binary and multinomial classification, aka single-label classification (SLC), such AutoML approaches have shown promising results. However, the task of multi-label classification (MLC), where data points are associated with a set of class labels instead of a single class label, has received much less attention …

abstract algorithms arxiv automated automated machine learning automl binary classification cs.lg dataset machine machine learning machine learning algorithms multinomial pipelines results supervised learning tasks them type