Affiliations: [a] Computer Science Department, University of Holguín, Holguín, Cuba | [b] Computer Science Department, Universidad Central de Las Villas, Santa Clara, Cuba | [c] Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain | [d] Information Systems Department, King Abdulaziz University, Jeddah, Saudi Arabia
Correspondence:
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Corresponding author: Sebastián Ventura, Department of Computer Science and Numerical Analysis, University of Córdoba, Albert Einstein Building, Rabanales Campus, Córdoba, Spain. Tel.: +34 957 212 218; Fax: +34 957 218 630; E-mail: [email protected].
Abstract: In the last decade several modern applications where the examples belong to more than one label at a time have attracted the attention of research into machine learning. Several derivatives of the k-nearest neighbours classifier to deal with multi-label data have been proposed. A k-nearest neighbours classifier has a high dependency with respect to the definition of a distance function, which is used to retrieve the k-nearest neighbours in feature space. The distance function is sensitive to irrelevant, redundant, and interacting or noise features that have a negative impact on the precision of the lazy algorithms. The performance of lazy algorithms can be significantly improved with the use of an appropriate weight vector, where a feature weight represents the ability of the feature to distinguish pattern classes. In this paper a filter-based feature weighting method to improve the performance of multi-label lazy algorithms is proposed. To learn the weights, an optimisation process of a metric is carried out as heuristic to estimate the feature weights. The experimental results on 21 multi-label datasets and 5 multi-label lazy algorithms confirm the effectiveness of the feature weighting method proposed for a better multi-label lazy learning.
