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Multi-label classification from high-speed data streams with adaptive model rules and random rules

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Abstract

Multi-label classification is a methodology that tries to solve classification problems where multiple classes are associated with each data example. Data streams pose new challenges to this methodology caused by the massive amounts of structured data production. In fact, most of the existent batch mode methods may not support this condition. Therefore, this paper proposes two multi-label classification methods based on rule and ensembles learning from continuous flow of data. These methods are derived from a multi-target regression algorithm. The main contribution of this work is the rule specialization for subsets of class labels, instead of the usual local (individual models for each output) or a global (one model for all outputs) methods. Prequential evaluation was conducted where global, local and subset operation modes were compared against other online classifiers found in the literature. Six real-world data sets were used. The evaluation demonstrated that the subset specialization presents competitive performance, when compared to local and global approaches and online classifiers found in the literature.

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Acknowledgements

This work is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961.

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  1. LIAAD-INESC Porto, University of Porto, Campus da FEUP, Rua Dr. Roberto Frias 378, 4200-465, Porto, Portugal

    Ricardo Sousa & João Gama

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  1. Ricardo Sousa
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  2. João Gama
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Correspondence to Ricardo Sousa.

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Sousa, R., Gama, J. Multi-label classification from high-speed data streams with adaptive model rules and random rules. Prog Artif Intell 7, 177–187 (2018). https://doi.org/10.1007/s13748-018-0142-z

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