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.
IntroductionMulti-label classification (MLC) is a classification task where an instance can be simultaneously classified in more than one of the existing classes. Labeled data extracted from several domains, like text, web pages, multimedia (audio, image, videos), and biology are intrinsically multi-labeled. Additionally, the number of application domains with MLC data is growing fast.Many current, real-world data science applications are MLC by nature. They are problems from very diverse domains, like labeling newspaper articles by subject and classification of proteins according to their functions. MLC algorithms have been successfully used in these and other MLC tasks (Diplaris et al., 2005). In a recent application, MLC algorithms were used to recommend food truck cuisines (Rivolli et al., 2017), assuming that a person can have more than one cuisine preference, and with the same level of preference.Despite its growing relevance, there is a lack of comprehensive and easy to use tools for the R environment. A tool frequently used in MLC experiments is MULAN (Tsoumakas et al., 2011), which is a Java library built on top of Weka (Hall et al., 2009) to allow Weka users to deal with MLC data. Its popularity in the research community can be attributed to its ease of use, its large number and variation of its functionalities. The MLC alternative to Python users is the scikit-multilearn (Szymański, 2017), which provides a set of MLC algorithms and an interface for the MULAN library. Although other simpler tools, like MEKA (Read et al., 2016) and general data mining software (Gibaja and Ventura, 2015) include good functionalities to deal with MLC tasks, they address few MLC features and are not available in R.It is important to mention that there are packages that offer some level of support for MLC in R. The most complete is the mldr package, an exploratory tool for the manipulation and analysis of MLC datasets (Charte and Charte, 2015). Although it does not contain MLC strategies, it supports the ARFF variation for MLC ...
