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

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Ricardo Sousa (Author) Other; João Gama (Author) FEP

Journal

Title: Progress in Artificial Intelligence

Vol. 7

Pages: 177-187

ISSN: 2192-6352

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 32 Citations

Other information

Authenticus ID: P-00N-KHC

DOI: 10.1007/s13748-018-0142-z

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11