Assigning several labels to digital data is becoming easier because we can perform it in a collaborative manner with Internet users. However, some suitable labels may be missed and may not be attached to the data leading to inaccuracies in classification. In this paper, we propose a novel graphbased multi-label classifier to support the multi-labeling task. The core process of our algorithm is ...

The problem of multi-label classification has attracted great interests in the last decade. Multi-label classification refers to the problems where an example that is represented by a single instance can be assigned tomore than one category. Until now, most of the researches on multi-label classification have focused on supervised settings whose assumption is that large amount of labeled traini...

Multi-label learning has become an important area of research due to the increasing number of modern applications that contain multi-label data. The multi-label data are structured in a more complex way than single-label data. Consequently the development of techniques that allow the improvement in the performance of machine learning algorithms over multi-label data is desired. The feature weig...

Multi-label problems arise frequently in image and video annotations, and many other related applications such as multi-topic text categorization, music classification, etc. Like other computer vision tasks, multi-label image and video annotations also suffer from the difficulty of high dimensionality because images often have a large number of features. Linear discriminant analysis (LDA) is a ...