Online Semi-supervised Growing Neural Gas for Multi-label Data Classification
Samira Boulbazine, Guénaël Cabanès, Basarab Mateï, Younès Bennani · 2018
In multi-label learning, each learning instance is associated with multiple class labels simultaneously and the task is to learn a transition from the features space to the labels space. Generally, it is costly and time consuming to get labels for learning samples, especially for the task of multi-label annotation where many class labels must be assigned to the same instance. To overcome this difficulty, semi-supervised multi-label learning aims to exploit unlabeled data readily available to help build a multi-label predictive model. Nevertheless, most semi-supervised solutions for the multi-label learning tasks use a batch method for labeling (e.g., label propagation), and are not suitable for online classification tasks. In this paper, a new online semi-supervised multi-label classifier based on the Growing Neural Gas (GNG) algorithm is presented. Online GNG-based algorithms are already used in several applications, but the existing algorithms are unable to tackle the problem of multi-label classification. There is a real need of online semi-supervised multi-label algorithms. Our main contribution is to propose an online multi-label algorithm based on GNG able to provide on the fly annotation of data without explicit storage of the training instances, i.e, with limited memory usage. The proposed algorithm is experimentally tested on five different data-sets from three different application domains. The performance results of the approach is compared with several state-of-the-art methods. The experiments show that the proposed Semi-Supervised algorithm outperforms the existing multi-label classifiers.