Towards a Transparent Deep Ensemble Method Based on Multiagent Argumentation

2019 ◽  
pp. 3-21
Author(s):  
Naziha Sendi ◽  
Nadia Abchiche-Mimouni ◽  
Farida Zehraoui
Keyword(s):  
Ensemble Method

Clustering ensemble method based on co-occurrence similarity

2011 ◽  
Vol 31 (2) ◽  
pp. 441-445 ◽  
Author(s):  
Guang LING ◽  
Ming-chun WANG ◽  
Jia-yi FENG
Keyword(s):  
Ensemble Method ◽  
Clustering Ensemble

scholarly journals An Optimized Stacking Ensemble Model for Phishing Websites Detection

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1285
Author(s):  
Mohammed Al-Sarem ◽  
Faisal Saeed ◽  
Zeyad Ghaleb Al-Mekhlafi ◽  
Badiea Abdulkarem Mohammed ◽  
Tawfik Al-Hadhrami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Ensemble Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Ensemble Model ◽  
Security Attacks ◽  
Data Set ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

scholarly journals Forecasting China's Service Outsourcing Development with an EMD-VAR-SVR Ensemble Method

2016 ◽  
Vol 91 ◽  
pp. 392-401 ◽  
Author(s):  
Siqi Yi ◽  
Kun Guo ◽  
Zhensong Chen
Keyword(s):  
Ensemble Method ◽  
Service Outsourcing

Clustering ensemble method based DILCA distance

2013 ◽  
Author(s):  
Bao-Ping Su ◽  
Ming-Chunwang ◽  
Yuan-Yuan Sun ◽  
Kun Liu
Keyword(s):  
Ensemble Method ◽  
Clustering Ensemble
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