Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …
performance. Currently, deep learning architectures are showing better performance …
A deep learning ensemble with data resampling for credit card fraud detection
Credit cards play an essential role in today's digital economy, and their usage has recently
grown tremendously, accompanied by a corresponding increase in credit card fraud …
grown tremendously, accompanied by a corresponding increase in credit card fraud …
An ensemble of a boosted hybrid of deep learning models and technical analysis for forecasting stock prices
For several years the modeling as well as forecasting of the prices of stocks have been
extremely challenging for the business community and researchers as a result of the …
extremely challenging for the business community and researchers as a result of the …
Dynamic ensemble learning for multi-label classification
Ensemble learning has been shown to be an effective approach to solve multi-label
classification problem. However, most existing ensemble learning methods do not consider …
classification problem. However, most existing ensemble learning methods do not consider …
Graph-based multi-label disease prediction model learning from medical data and domain knowledge
In recent years, the means of disease diagnosis and treatment have been improved
remarkably, along with the continuous development of technology and science …
remarkably, along with the continuous development of technology and science …
XRR: Extreme multi-label text classification with candidate retrieving and deep ranking
Abstract Extreme Multi-label Text Classification (XMTC) is a key task of finding the most
relevant labels from a large label set for a document. Although some deep learning-based …
relevant labels from a large label set for a document. Although some deep learning-based …
A review of regression and classification techniques for analysis of common and rare variants and gene-environmental factors
Statistical techniques incorporated with machine-learning algorithms in unison with gene-
environment interaction are giving unparalleled understanding of complex diseases …
environment interaction are giving unparalleled understanding of complex diseases …
An emotion role mining approach based on multiview ensemble learning in social networks
Y Du, Y Wang, J Hu, X Li, X Chen - Information fusion, 2022 - Elsevier
Emotion is a status that combines people's feelings, thoughts, and behaviors, and plays a
crucial role in communication among people. Large studies suggest that human emotions …
crucial role in communication among people. Large studies suggest that human emotions …
Feature construction and smote-based imbalance handling for multi-label learning
The class-imbalance is intrinsic in Multi-label datasets due to the higher number of labels,
few relevant labels in many instances, and a varied number of relevant instances for …
few relevant labels in many instances, and a varied number of relevant instances for …
Class-driven graph attention network for multi-label time series classification in mobile health digital twins
Digital Twins for Mobile Networks (DTMN) can enhance mobile health (mHealth) by
increasing diagnostic and monitoring capabilities. Classifying multi-label time series …
increasing diagnostic and monitoring capabilities. Classifying multi-label time series …