Lazy Multi-label Learning Algorithms Based on Mutuality Strategies

• Published in: Journal of intelligent & robotic systems Vol. 80; no. Suppl 1; pp. 261 - 276
• Main Authors: Cherman, Everton Alvares, Spolaôr, Newton, Valverde-Rebaza, Jorge, Monard, Maria Carolina
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2015; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Benchmarking; Communities; Control; Criteria; Electrical Engineering; Engineering; Labels; Learning; Machine learning; Mechanical Engineering; Mechatronics; Performance evaluation; Robotics; Strategy; Voting; Multi-label learning; Lazy algorithms; Nearest Neighbors; Machine learning
• ISSN: 0921-0296; 1573-0409; 1573-0409
• DOI: 10.1007/s10846-014-0144-4

Lazy multi-label learning algorithms have become an important research topic within the multi-label community. These algorithms usually consider the set of standard k -Nearest Neighbors of a new instance to predict its labels (multi-label). The prediction is made by following a voting criteria within the multi-labels of the set of k -Nearest Neighbors of the new instance. This work proposes the use of two alternative strategies to identify the set of these examples: the Mutual and Not Mutual Nearest Neighbors rules, which have already been used by lazy single-learning algorithms. In this work, we use these strategies to extend the lazy multi-label algorithm BRkNN . An experimental evaluation carried out to compare both mutuality strategies with the original BRkNN algorithm and the well-known MLkNN lazy algorithm on 15 benchmark datasets showed that MLkNN presented the best predictive performance for the Hamming-Loss evaluation measure, although it was significantly outperformed by the mutuality strategies when F-Measure is considered. The best results of the lazy algorithms were also compared with the results obtained by the Binary Relevance approach using three different base learning algorithms.