The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

With the continuous expansion of data availability in many large-scale, complex, and
networked systems, such as surveillance, security, Internet, and finance, it becomes critical …

Extreme learning machine (ELM) is a competitive machine learning technique, which is
simple in theory and fast in implementation. The network types are “generalized” single …

Traditional classification algorithms usually provide poor accuracy on the prediction of the
minority class of imbalanced data sets. This paper proposes a new method for dealing with …

Video indexing and retrieval have a wide spectrum of promising applications, motivating the
interest of researchers worldwide. This paper offers a tutorial and an overview of the …

In this paper, we propose a new image clustering algorithm, referred to as clustering using
local discriminant models and global integration (LDMGI). To deal with the data points …

Support vector machines (SVMs) is a very popular machine learning technique. An SVM
classifier trained on an imbalanced dataset can produce suboptimal models that are biased …

In recent years, learning from imbalanced data has attracted growing attention from both
academia and industry due to the explosive growth of applications that use and produce …

Balanced dataset has been utilized by the previous human activity recognition algorithms to
train the classifier. However, imbalanced dataset are ubiquitous in human activity …

Considering the challenges of using SVM to learn concepts from large-scale imbalanced
datasets, we proposed a new method: Boosted Near-miss Under-sampling on SVM …