Machine learning involves the task of training systems to be able make decisions without being explicitly programmed. Important among machine tasks is classification involving process machines predictions from predefined labels. Classification broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an appli...

This paper is devoted to the multi-topic (multilabel) text classification problem. We propose two methods for reduction from ranking to the multi-label case. Unlike existing multi-label classification methods based on reduction from ranking problem, where the complex classification (threshold) function is being defined on the input feature space, in our approach we propose the construction of s...

Multi-label image classification is of significant interest due to its major role in real-world web image analysis applications such as large-scale image retrieval and browsing. Recently, matrix completion (MC) has been developed to deal with multi-label classification tasks. MC has distinct advantages, such as robustness to missing entries in the feature and label spaces and a natural ability ...

Abstract Sub-class partition information within positive and negative classes is often ignored by a binary classifier, even when these detailed background information is available at hand. It is expected that this kind of additional information can help to improve the differentiating capacity of a binary classifier. In this paper, a binary classification strategy via multi-class categorization ...

For multi-label image classification, we use active learning to select examplelabel pairs to acquire labels from experts. The core of active learning is to select the most informative examples to request their labels. Most previous studies in active learning for multi-label classification have two shortcomings. One is that they didn't pay enough attention on label correlations. The other shortc...