In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …

Despite more than two decades of continuous development learning from imbalanced data
is still a focus of intense research. Starting as a problem of skewed distributions of binary …

Classification of imbalanced data is a vastly explored issue of the last and present decade
and still keeps the same importance because data are an essential term today and it …

This paper proposes a human action recognition method via coupled hidden conditional
random fields model by fusing both RGB and depth sequential information. The coupled …

Human action may be observed from multi-view, which are highly related but sometimes
look different from each other. Traditional metric learning algorithms have achieved …

The study of machine learning has revealed that it can unleash new applications in a variety
of disciplines. Many limitations limit their expressiveness, and researchers are working to …

Class imbalance occurs when data elements are unevenly distributed among classes, which
poses a challenge for classifiers. The core focus of the research community has been on …

Datasets are growing in size and complexity at a pace never seen before, forming ever
larger datasets known as Big Data. A common problem for classification, especially in Big …

Although multiple methods have been proposed for human action recognition, the existing
multi-view approaches cannot well discover meaningful relationship among multiple action …

Multi-modalities action recognition becomes a hot research topic, and this paper proposes a
collaborative sparse representation leaning model for RGB-D action recognition where RGB …