Distributed heterogeneous ensemble learning on Apache Spark for ligand-based virtual screening

2021 ◽  
Vol 13 (1/2) ◽  
pp. 160
Author(s):  
Karima Sid ◽  
Mohamed Batouche
Keyword(s):  
Virtual Screening ◽  
Ensemble Learning ◽  
Apache Spark ◽  
Heterogeneous Ensemble

scholarly journals Improved method of structure-based virtual screening based on ensemble learning

RSC Advances ◽  
2020 ◽  
Vol 10 (13) ◽  
pp. 7609-7618
Author(s):  
Jin Li ◽  
WeiChao Liu ◽  
Yongping Song ◽  
JiYi Xia
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Ensemble Learning ◽  
High Throughput ◽  
High Throughput Screening ◽  
Screening Method ◽  
Improved Method ◽  
Complementary Technique

Virtual screening has become a successful alternative and complementary technique to experimental high-throughput screening technologies for drug design. This paper proposed a target-specific virtual screening method based on ensemble learning named ENS-VS.

HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning

2020 ◽  
Vol 169 ◽  
pp. 107049 ◽  
Author(s):  
Ying Zhong ◽  
Wenqi Chen ◽  
Zhiliang Wang ◽  
Yifan Chen ◽  
Kai Wang ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Ensemble Learning ◽  
Detection Model ◽  
Model Based ◽  
Heterogeneous Ensemble ◽  
Network Anomaly Detection

scholarly journals A two-layer nested heterogeneous ensemble learning predictive method for COVID-19 mortality

2021 ◽  
pp. 107946
Author(s):  
Shaoze Cui ◽  
Yanzhang Wang ◽  
Dujuan Wang ◽  
Qian Sai ◽  
Ziheng Huang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Predictive Method ◽  
Heterogeneous Ensemble

Heterogeneous ensemble learning with feature engineering for default prediction in peer-to-peer lending in China

2019 ◽  
Vol 23 (1) ◽  
pp. 23-45 ◽  
Author(s):  
Wei Li ◽  
Shuai Ding ◽  
Hao Wang ◽  
Yi Chen ◽  
Shanlin Yang
Keyword(s):  
Ensemble Learning ◽  
Peer To Peer ◽  
Feature Engineering ◽  
Default Prediction ◽  
Peer Lending ◽  
Heterogeneous Ensemble

scholarly journals PreNNsem: A Heterogeneous Ensemble Learning Framework for Vulnerability Detection in Software

2020 ◽  
Vol 10 (22) ◽  
pp. 7954
Author(s):  
Lu Wang ◽  
Xin Li ◽  
Ruiheng Wang ◽  
Yang Xin ◽  
Mingcheng Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ensemble Learning ◽  
Detection Efficiency ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Training Methods ◽  
Vulnerability Detection ◽  
Sample Data ◽  
Heterogeneous Ensemble

Automated vulnerability detection is one of the critical issues in the realm of software security. Existing solutions to this problem are mostly based on features that are defined by human experts and directly lead to missed potential vulnerability. Deep learning is an effective method for automating the extraction of vulnerability characteristics. Our paper proposes intelligent and automated vulnerability detection while using deep representation learning and heterogeneous ensemble learning. Firstly, we transform sample data from source code by removing segments that are unrelated to the vulnerability in order to reduce code analysis and improve detection efficiency in our experiments. Secondly, we represent the sample data as real vectors by pre-training on the corpus and maintaining its semantic information. Thirdly, the vectors are fed to a deep learning model to obtain the features of vulnerability. Lastly, we train a heterogeneous ensemble classifier. We analyze the effectiveness and resource consumption of different network models, pre-training methods, classifiers, and vulnerabilities separately in order to evaluate the detection method. We also compare our approach with some well-known vulnerability detection commercial tools and academic methods. The experimental results show that our proposed method provides improvements in false positive rate, false negative rate, precision, recall, and F1 score.

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