摘要：Learning classification tasks in which each instance is associated with one or more labels are known as multi-label learning. The implementation of multi-label algorithms, performed by different researchers, have several specificities, like input/output format, different internal functions, distinct programming language, to mention just some of them. As a result, current machine learning tools include only a small subset of multi-label decomposition strategies. The utiml package is a framework for the application of classification algorithms to multi-label data. Like the well known MULAN used with Weka, it provides a set of multi-label procedures such as sampling methods, transformation strategies, threshold functions, pre-processing techniques and evaluation metrics. The package was designed to allow users to easily perform complete multi-label classification experiments in the R environment. This paper describes the utiml API and illustrates its use in different multi-label classification scenarios.
