Ensemble methods use multiple models to get better performance. Ensemble methods have
been used in multiple research fields such as computational intelligence, statistics and …

Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

It is well-known that exploiting label correlations is important to multi-label learning. Existing
approaches either assume that the label correlations are global and shared by all instances; …

Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …

Shale gas is an important unconventional natural gas resource existing in shale reservoir
with huge reserves. Due to the ultralow porosity and permeability, it requires the horizontal …

Multi-label learning 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 learning …

A variety of approaches have been recently proposed to automatically infer users'
personality from their user generated content in social media. Approaches differ in terms of …

In many practical applications of supervised learning the task involves the prediction of
multiple target variables from a common set of input variables. When the prediction targets …

A multi-label classification based approach for sentiment analysis is proposed in this paper.
To the best of our knowledge, this work is the first to propose to use multi-label classification …

Recommending phrases from web pages for advertisers to bid on against search engine
queries is an important research problem with direct commercial impact. Most approaches …