Online Multi-label Classification with Adaptive Model Rules

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

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

Conference proceedings International

Title: ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2016

Pages: 58-67

17th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA)

Salamanca, SPAIN, SEP 14-16, 2016

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 3 Citations

Other information

Authenticus ID: P-00K-VAY

DOI: 10.1007/978-3-319-44636-3_6

Abstract (EN): The interest on online classification has been increasing due to data streams systems growth and the need for Multi-label Classification applications have followed the same trend. However, most of classification methods are not performed on-line. Moreover, data streams produce huge amounts of data and the available processing resources may not be sufficient. This work-in-progress paper proposes an algorithm for Multi-label Classification applications in data streams scenarios. The proposed method is derived from multi-target structured regressor AMRules that produces models using subsets of output attributes (output specialization strategy). Performance tests were conducted where the operation modes global, local and subset approaches of the proposed method were compared to each other and to others online multi-label classifiers described in the literature. Three datasets of real scenarios were used for evaluation. The results indicate that the subset specialization mode is competitive in comparison to local and global approaches and to other online multi-label classifiers.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10