Multi-label learning deals with problems where each example is represented by a single
instance while being associated with multiple class labels simultaneously. Binary relevance …

The family of methods collectively known as classifier chains has become a popular
approach to multi-label learning problems. This approach involves chaining together off-the …

This paper studies the (often implicit) human values behind natural language arguments,
such as to have freedom of thought or to be broadminded. Values are commonly accepted …

Understanding the dynamics of deforestation and land uses of neighboring areas is of vital
importance for the design and development of appropriate forest conservation and …

Many problem settings in machine learning are concerned with the simultaneous prediction
of multiple target variables of diverse type. Amongst others, such problem settings arise in …

In multi-label learning, different labels may have their own inherent characteristics for
distinguishing each other, in the meanwhile, exploiting the correlations among labels is …

Label ambiguity and data complexity are widely recognized as major challenges in multi-
label classification. Existing studies strive to find approximate representations concerning …

In the past decades, the ensemble systems have been shown as an efficient method to
increase the accuracy and stability of classification algorithms. However, how to get a valid …

Nondestructive detection of mutton freshness based on a single indicator has limitations and
poor applicability. Hyperspectral imaging technology can detect a variety of information in …

Large datasets computing is a research problem as well as a huge challenge due to
massive amounts of data that are mined and crunched in order to successfully analyze these …