Neighbor selection for multilabel classification

Abstract

kNN is extensively studied for multilabel classification in the literature. Several kNN-based multilabel learning algorithms have been witnessed during the past years. They usually take kNN as their base classifiers to construct classification models, and then predict the class labels by virtue of Bayesian or majority rules. In this paper, a nearest neighbor selection for multilabel classification is proposed. Specifically, the target labels of new data are predicted with the help of those relevant and reliable data, which explored by the concept of shelly nearest neighbor. For effectiveness, the certainty factor is further adopted to well address the problem of unbalanced and uncertain data. The comparison experiments with eleven popular multilabel classifiers are conducted on ten benchmark data sets. The experimental results show that the performance of the proposed method is competitive and outperforms the popular multilabel classifiers in most cases.