Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

Multi-view learning is an emerging direction in machine learning which considers learning
with multiple views to improve the generalization performance. Multi-view learning is also …

Gesture recognition using sparse multichannel surface electromyography (sEMG) is a
challenging problem, and the solutions are far from optimal from the point of view of muscle …

Due to complex semantics, a sample may be associated with multiple labels in various
classification and recognition tasks. Multilabel learning generates training models to map …

Multilabel learning deals with examples having multiple class labels simultaneously. It has
been applied to a variety of applications, such as text categorization and image annotation …

When tackling high dimensionality in data mining, online feature selection which deals with
features flowing in one by one over time, presents more advantages than traditional feature …

Hyperspectral images (HSI) are obtained from hyperspectral imaging sensors to capture the
object's information in hundreds of spectral bands. However, how to make full advantage of …

In multi-label learning, label correlations commonly exist in the data. Such correlation not
only provides useful information, but also imposes significant challenges for multi-label …

Multi-label data with high dimensionality, widely existed in the real world, bring many
challenges to the applications of machine learning, pattern recognition and other fields …

As social media are continually gaining more popularity, they have become an important
source for manufacturers to collect information related to defects on their products from …