Lazy Multi-label Learning Algorithms Based on Mutuality Strategies

Author(s)

Cherman, Everton Alvares ; Spolaor, Newton ; Valverde-Rebaza, Jorge ; Monard, Maria Carolina

• Document type: Journal article
• Source: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS; v. 80, p. 16-pg., 2015-12-01.

Abstract

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. (AU)

• FAPESP's process: 13/12191-5 - Mining User Behavior in Location-Based Social Networks
• Grantee: Jorge Carlos Valverde Rebaza
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 10/15992-0 - Exploring label dependency in multilabel learning
• Grantee: Everton Alvares Cherman
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• FAPESP's process: 11/02393-4 - Feature Selection for Multi-label Learning
• Grantee: Newton Spolaôr
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications
• Grantee: Luis Gustavo Nonato
• Support Opportunities: Research Projects - Thematic Grants