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

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.