Working with Multilabel Datasets in R: The mldr Package

Most classification algorithms deal with datasets which have a set of input features, the variables to be used as predictors, and only one output class, the variable to be predicted. However, in late years many scenarios in which the classifier has to work with several outputs have come to life. Automatic labeling of text documents, image annotation or protein classification are among them. Multilabel datasets are the product of these new needs, and they have many specific traits. The mldr package allows the user to load datasets of this kind, obtain their characteristics, produce specialized plots, and manipulate them. The goal is to provide the exploratory tools needed to analyze multilabel datasets, as well as the transformation and manipulation functions that will make possible to apply binary and multiclass classification models to this data or the development of new multilabel classifiers. Thanks to its integrated user interface, the exploratory functions will be available even to non-specialized R users. The mldr package aims to provide the user with the functions needed to perform exploratory analysis of MLDs, determining their main traits both statistically and visually. Moreover, it also brings the proper tools to manipulate this kind of datasets, including the application of the most common transformation methods, BR (Binary Relevance) and LP (Label Powerset), that will be described in the following section. These would be the foundation for processing MLDs with traditional classifiers, as well as for developing new multilabel algorithms. The mldr package does not depend on the RWeka package, and it is not linked to MULAN nor MEKA. It has been designed to allow reading both MULAN and MEKA MLDs, but without any external dependencies. In fact, it would be possible to load MLDs stored in other file formats, as well as creating them from scratch. When loaded, MLDs are wrapped in an S3 type object with class "mldr", which allows for the use of methods. The object will contain the data in the MLD and also a large set of measures obtained from it. The functions provided by the package ease the access to

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