AI on the edge: a comprehensive review
W Su, L Li, F Liu, M He, X Liang - Artificial Intelligence Review, 2022 - Springer
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 …
on data centers and network bandwidth. To ease the pressure on data centers, edge …
Feature selection techniques in the context of big data: taxonomy and analysis
HM Abdulwahab, S Ajitha, MAN Saif - Applied Intelligence, 2022 - Springer
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 …
production of big data, as enormous volumes of data with high dimensional features grow …
[HTML][HTML] Dual regularized unsupervised feature selection based on matrix factorization and minimum redundancy with application in gene selection
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 …
computational biology over the past few years. In the field of gene expression analysis …
Neural network systems with an integrated coefficient of variation-based feature selection for stock price and trend prediction
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 …
market analyses can be integrated with historical stock market data to derive a set of …
Multi-objective PSO based online feature selection for multi-label classification
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 …
data to improve the efficiency without degrading the performance of the model. In many real …
Multi-strategy ensemble binary hunger games search for feature selection
BJ Ma, S Liu, AA Heidari - Knowledge-Based Systems, 2022 - Elsevier
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 …
mining, devoted to reducing the data dimensionality to improve the performance of learning …
A class-aware supervised contrastive learning framework for imbalanced fault diagnosis
J Zhang, J Zou, Z Su, J Tang, Y Kang, H Xu… - Knowledge-Based …, 2022 - Elsevier
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 …
meet severe performance degradation when the number of samples for fault classes is much …
Graph-based class-imbalance learning with label enhancement
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 …
The class-imbalance distribution can make most classical classification algorithms neglect …
Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods
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 …
construction accidents. Although distinctive progress has been made in safety management …
Learning from class-imbalanced data with a model-agnostic framework for machine intelligent diagnosis
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 …
scale equipment, classification with these class-imbalanced datasets can lead to the …