Fake news detection using an ensemble learning model based on Self-Adaptive Harmony Search algorithms

2020 ◽  
Vol 159 ◽  
pp. 113584
Author(s):  
Yin-Fu Huang ◽  
Po-Hong Chen
Keyword(s):  
Ensemble Learning ◽  
Harmony Search ◽  
Learning Model ◽  
Search Algorithms ◽  
Fake News ◽  
Model Based ◽  
Self Adaptive

A multi-instance ensemble learning model based on concept lattice

2011 ◽  
Vol 24 (8) ◽  
pp. 1203-1213 ◽  
Author(s):  
Xiangping Kang ◽  
Deyu Li ◽  
Suge Wang
Keyword(s):  
Ensemble Learning ◽  
Concept Lattice ◽  
Learning Model ◽  
Model Based

A multi-factor and high-order stock forecast model based on Type-2 FTS using cuckoo search and self-adaptive harmony search

2017 ◽  
Vol 240 ◽  
pp. 13-24 ◽  
Author(s):  
Wenyu Zhang ◽  
Shixiong Zhang ◽  
Shuai Zhang ◽  
Dejian Yu ◽  
NingNing Huang
Keyword(s):  
Harmony Search ◽  
Cuckoo Search ◽  
Forecast Model ◽  
High Order ◽  
Model Based ◽  
Self Adaptive

A Decomposition-Ensemble Learning Model Based on LSTM Neural Network for Daily Reservoir Inflow Forecasting

2019 ◽  
Vol 33 (12) ◽  
pp. 4123-4139 ◽  
Author(s):  
Yutao Qi ◽  
Zhanao Zhou ◽  
Lingling Yang ◽  
Yining Quan ◽  
Qiguang Miao
Keyword(s):  
Neural Network ◽  
Ensemble Learning ◽  
Learning Model ◽  
Model Based ◽  
Inflow Forecasting ◽  
Reservoir Inflow

scholarly journals A Heterogeneous Ensemble Learning Model Based on Data Distribution for Credit Card Fraud Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yalong Xie ◽  
Aiping Li ◽  
Liqun Gao ◽  
Ziniu Liu
Keyword(s):  
Ensemble Learning ◽  
Credit Card ◽  
Data Distribution ◽  
Fraud Detection ◽  
Learning Model ◽  
Minority Class ◽  
Credit Card Fraud ◽  
Model Based ◽  
Savings Rate ◽  
Heterogeneous Ensemble

Credit card fraud detection (CCFD) is important for protecting the cardholder’s property and the reputation of banks. Class imbalance in credit card transaction data is a primary factor affecting the classification performance of current detection models. However, prior approaches are aimed at improving the prediction accuracy of the minority class samples (fraudulent transactions), but this usually leads to a significant drop in the model’s predictive performance for the majority class samples (legal transactions), which greatly increases the investigation cost for banks. In this paper, we propose a heterogeneous ensemble learning model based on data distribution (HELMDD) to deal with imbalanced data in CCFD. We validate the effectiveness of HELMDD on two real credit card datasets. The experimental results demonstrate that compared with current state-of-the-art models, HELMDD has the best comprehensive performance. HELMDD not only achieves good recall rates for both the minority class and the majority class but also increases the savings rate for banks to 0.8623 and 0.6696, respectively.

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