Evolutionary feature weighting to improve the performance of multi-label lazy algorithms

• Published in: Integrated computer-aided engineering Vol. 21; no. 4; pp. 339 - 354
• Main Authors: Reyes, Oscar, Morell, Carlos, Ventura, Sebastián
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2014
• Subjects: Algorithms; Classifiers; Derivatives; Evolutionary; Mathematical analysis; Mathematical models; Performance enhancement; Weighting methods; lazy learning algorithms; multi-label classification; learning metric; label ranking; evolutionary algorithms; Feature weighting
• ISSN: 1069-2509; 1875-8835
• DOI: 10.3233/ICA-140468

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.