With the advent of the Internet of Everything, the proliferation of data has put a huge burden
on data centers and network bandwidth. To ease the pressure on data centers, edge …

Abstract Recent advancements in Information Technology (IT) have engendered the rapid
production of big data, as enormous volumes of data with high dimensional features grow …

Gene expression data have become increasingly important in machine learning and
computational biology over the past few years. In the field of gene expression analysis …

Stock market forecasting has been a subject of interest for many researchers; the essential
market analyses can be integrated with historical stock market data to derive a set of …

Feature selection approaches aim to select a set of prominent features that best describe the
data to improve the efficiency without degrading the performance of the model. In many real …

Feature selection is a crucial preprocessing step in the sphere of machine learning and data
mining, devoted to reducing the data dimensionality to improve the performance of learning …

Deep learning-based fault diagnosis models constructed from imbalanced datasets would
meet severe performance degradation when the number of samples for fault classes is much …

Class imbalance is a common issue in the community of machine learning and data mining.
The class-imbalance distribution can make most classical classification algorithms neglect …

Purpose Central to the entire discipline of construction safety management is the concept of
construction accidents. Although distinctive progress has been made in safety management …

Considering the difficulty of data acquisition in industry, especially for failure data of large-
scale equipment, classification with these class-imbalanced datasets can lead to the …