Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...

A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label. Textual data, such as documents and web pages, are frequently annotat...

Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...

This paper describes the submission of the University of Washington’s Center for Data Science to the PAN 2014 author profiling task. We examine the predictive quality in terms of age and gender of several sets of features extracted from various genres of online social media. Through comparison, we establish a feature set which maximizes accuracy of gender and age prediction across all genres ex...

There are many available methods for generating synthetic data streams. Such methods have been justified by the need to study the efficacy of algorithms on a theoretically infinite stream, and also a lack of real-world data of sufficient size. Although multi-label classification has attracted considerable interest in recent years, most of this work has been carried out in the context of a batch...

Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data. Yet, there no method for effectively generating stratified partitions XML datasets. Instead, researchers typically rely on provided test-train splits that, 1) aren’t always representative entire dataset, and 2) are missing many labels. This can lead to poor generalization ability unreliable perfor...

In classification problems, a pattern may belong to one or multiple categories. It is essential to deal multi-label classification accurately and efficiently. Threshold strategies can be used for multi-label classification. We propose four schemes to compute threshold for a threshold based multi-label classification. We validate our method using multi-label text data and multi-label image data....