Abstract
Multilabel classification and other learning from multilabeled data tasks are relatively recent, with barely a decade of history behind them. When compared against binary and multiclass learning, the range of available datasets, frameworks, and other software tools is significantly more scarce. The goal of this last chapter is to provide the reader with the proper insight to take advantage of these software tools. A brief overview of them is offered in Sect. 9.1. Section 9.2 discusses the different multilabel file formats, enumerates the data repositories the MLDs can be downloaded from, and describes how to automate some tasks with the mldr.datasets R package. How to perform exploratory data analysis of MLDs is the main topic of Sect. 9.3. Then, the process to conduct experiments with multilabel data using different tools is outlined in Sect. 9.4.
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Notes
- 1.
An ARFF file is usually divided into three sections. The first one contains the name of the dataset after de @relation tag, the second one provides information about the attributes with @attribute tags, and the third one, whose beginning is marked with the @data tag, contains the actual data. It is the file format used by the popular WEKA data mining tool.
- 2.
The number of MLDs provided by each repository has been checked as of April 2016.
- 3.
The [] operator defined in the mldr package is designed to work with mldr objects only. The standard [] R operator can be used over the mldr$dataset member to manipulate the raw multilabel data.
- 4.
The C4.5 algorithm is implemented in WEKA by the J48 class.
- 5.
Although due to the page width limit the sentence appears in the text divided into two lines, it has to be entered as only one.
References
Tsoumakas, G., Xioufis, E.S., Vilcek, J., Vlahavas, I.: MULAN: A Java library for multi-label learning. J. Mach. Learn. Res. 12, 2411–2414 (2011)
Read, J., Reutemann, P.: MEKA multi-label dataset repository. http://sourceforge.net/projects/meka/files/Datasets/
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)
Alcala-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: data set repository and integration of algorithms and experimental analysis framework. J. Mult-Valued Log. Soft Comput. 17(2–3), 255–287 (2011)
Charte, F., Charte, D., Rivera, A.J., del Jesus, M.J., Herrera, F.: R Ultimate Multilabel Dataset Repository. https://github.com/fcharte/mldr.datasets
Tsoumakas, G., Xioufis, E.S., Vilcek, J., Vlahavas, I.: MULAN multi-label dataset repository. http://mulan.sourceforge.net/datasets.html
Chang, C.C., Lin, C.J.: LIBSVM data: multi-label classification repository. http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html
Alcala-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL multi-label dataset repository. http://sci2s.ugr.es/keel/multilabel.php
Charte, F., Charte, D., Rivera, A.J., del Jesus, M.J., Herrera, F.: R ultimate multilabel dataset repository. In: Proceeedings of 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS’16, vol. 9648, pp. 487–499. Springer (2016)
Bhatia, K.H., Jain, P.J., Varma, M.: The extreme classification repository: multi-label datasets & code. http://research.microsoft.com/en-us/um/people/manik/downloads/XC/XMLRepository.html
R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2014). http://www.R-project.org/
Tomás, J.T., Spolaôr, N., Cherman, E.A., Monard, M.C.: A framework to generate synthetic multi-label datasets. Electron. Notes Theor. Comput. Sci. 302, 155–176 (2014)
Read, J., Pfahringer, B., Holmes, G.: Generating synthetic multi-label data streams. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD’09, pp. 69–84 (2009)
Charte, F., Charte, D.: Working with multilabel datasets in R: the mldr package. R J. 7(2), 149–162 (2015)
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Herrera, F., Charte, F., Rivera, A.J., del Jesus, M.J. (2016). Multilabel Software. In: Multilabel Classification . Springer, Cham. https://doi.org/10.1007/978-3-319-41111-8_9
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