Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves 2 possible label sets when the label dimension L is very large, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by building and modeling an explicit labe...

Article history: Available online 20 November 2013

We combine multi-task learning and semisupervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence classification tasks with disparate label spaces. We outperform strong single an...

Predicting the subcellular localization of proteins is an important and challenging problem. Traditional experimental approaches are often expensive and time-consuming. Consequently, a growing number of research efforts employ a series of machine learning approaches to predict the subcellular location of proteins. There are two main challenges among the state-of-the-art prediction methods. Firs...

Classifying text data has been an active area of research for a long time. Text document is multifaceted object and often inherently ambiguous by nature. Multi-label learning deals with such ambiguous object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input document. Traditional single label and multi class text cla...

Large classifier systems are machine learning algorithms that use multiple classifiers to improve the prediction of target values in advanced classification tasks. Although learning problems in bioand cheminformatics commonly provide data in schemes suitable for large classifier systems, they are rarely used in these domains. This thesis introduces two new classifiers incorporating systems of c...

Automatic image annotation has attracted lots of research interest, and effective method for image annotation. Find effectively the correlation among labels and images is a critical task for multi-label learning. Most of the existing multi-label learning methods exploit the label correlation only in the output label space, leaving the connection between label and features of images untouched. I...

New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years. The experimentation associated with each of them always goes through the same phases: selection of datasets, partitioning, training, analysis of results and, finally, comparison with existing methods. This last step is often hampered since it involves using exactly the sam...

Classifying text data has been an active area of research for a long time. Text document is multifaceted object and often inherently ambiguous by nature. Multi-label learning deals with such ambiguous object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input document. Traditional single label and multi class text cla...

In many real-world applications, humangenerated data like images are often associated with several semantic topics simultaneously, called multi-label data, which poses a great challenge for classification in such scenarios. Since the topics are always not independent, it is very useful to respect the correlations among different topics for performing better classification on multi-label data. H...