As a generalized form of multi-class classification, multilabel classification allows each sample to be associated with multiple labels. This task becomes challenging when the number of labels bulks up, which demands a high efficiency. Many approaches have been proposed to address this problem, among which one of the main ideas is to select a subset of labels which can approximately span the or...

Facial Attribute Classification (FAC) has attracted increasing attention in computer vision and pattern recognition. However, state-of-the-art FAC methods perform face detection/alignment independently. The inherent dependencies between these tasks are not fully exploited. In addition, most predict all facial attributes using the same CNN network architecture, which ignores different learning c...

Motivated by an increasing number of new applications, the research community is devoting an increasing amount of attention to the task of multi-label classification (MLC). Many different approaches to solving multi-label classification problems have been recently developed. Recent empirical studies have comprehensively evaluated many of these approaches on many datasets using different evaluat...

Multi-label classification has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label classification methods. These methods either transform the multi-label dataset to several simpler datasets or adapt the learning algorithm so it can handle the multiple labels. In this paper, we consider the latter appr...

When a sample belongs to more than one label from a set of available classes, the classification problem (known as multi-label classification) turns to be more complicated. Text data, widely available nowadays in the world wide web, is an obvious instance example of such a task. This paper presents a new method for multi-label text categorization created by modifying the Error-Correcting Output...

A web page is a complex document which can share conventions of several genres, or contain several parts, each belonging to a different genre. To properly address the genre interplay, a recent proposal in automatic web genre identification is multi-label classification. The dominant approach to such classification is to transform one multi-label machine learning problem into several sub-problem...

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 is propos...

A common way of attacking multi-label classification problems is by splitting it into a set of binary classification problems, then solving each problem independently using traditional single-label methods. Nevertheless, by learning classifiers separately the information about the relationship between labels tends to be neglected. Built on recent advances in structure learning in Ising Markov R...

Text classification, the task of metadata to documents, requires significant time and effort when performed by humans. Moreover, with online-generated content explosively growing, it becomes a challenge for manually annotating with large scale and unstructured data. Currently, lots of state-or-art text mining methods have been applied to classification process, many of them based on the key wor...