In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …

Anomaly detection is a critical issue across several academic fields and real-world
applications. Artificial neural networks have been proposed to detect anomalies from …

In the past decade, deep learning techniques have powered many aspects of our daily life,
and drawn ever-increasing research interests. However, conventional deep learning …

With the vigorous development of Industry 4.0, industrial Big Data has turned into the core
element of the Industrial Internet of Things. As one of the most fundamental and …

The scalp electroencephalogram (EEG)-based epileptic seizure/nonseizure detection has
been comprehensively studied, and fruitful achievements have been reported in the past …

One-class support vector machine (OC-SVM) is a common algorithm to solve one-class
classification (OCC) problem. Weighted OC-SVM (WOC-SVM) is an improved algorithm …

We address a new numerical method based on a class of machine learning methods, the so-
called Extreme Learning Machines (ELM) with both sigmoidal and radial-basis functions, for …

Abstract The Extreme Learning Machine is a single-hidden-layer feedforward learning
algorithm, which has been successfully applied in regression and classification problems in …

Recent years have witnessed the development of the applications of machine learning
technologies to well logging-based lithology identification. Most of the existing work …

Epilepsy ictal detection based on scalp electroencephalograms (EEGs) has been
comprehensively studied in the past decades. But few attentions have been paid to the …