Results 41 to 50 of about 10,458 (157)

Stacked ensemble learning for range-separation parameters [PDF]

Proceedings of the 2021 International Symposium on Molecular Spectroscopy, 2021
Zhou Lin, Alexander Kohn, Nadav Geva, Ethan French, Cheng-Wei Ju   +4 more
openaire   +1 more source

Thaw Slump Susceptibility Mapping Based on Sample Optimization and Ensemble Learning Techniques in Qinghai-Tibet Railway Corridor

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Thaw slump susceptibility mapping (TSSM) of Qinghai–Tibet railway corridor (QTRC) is the prerequisite and basis for disaster assessment and prevention of permafrost projects.
Yi He, Tianbao Huo, Binghai Gao, Qing Zhu, Long Jin, Jian Chen, Qing Zhang, Jiapeng Tang   +7 more
doaj   +1 more source

Multi-View Stacking Ensemble for Power Consumption Anomaly Detection in the Context of Industrial Internet of Things

IEEE Access, 2018
Anomaly detection of power consumption, mainly including electricity stealing and unexpected power energy loss, has been one of the essential routine works in power system management and maintenance.
Zhiyou Ouyang, Xiaokui Sun, Jingang Chen, Dong Yue, Tengfei Zhang   +4 more
doaj   +1 more source

A multi-level classification model for corrosion defects in oil and gas pipelines using meta-learner ensemble (MLE) techniques

Journal of Pipeline Science and Engineering
Maintaining the integrity of oil and gas pipelines is necessary for the efficient and safe transport of hydrocarbons. Corrosion defects can lead to decreased operational efficiency, leaks, a reduction in operational efficiency, and even catastrophic ...
Adamu Abubakar Sani, Mohamed Mubarak Abdul Wahab, Nasir Shafiq, Kamaludden Usman Danyaro, Nasir Khan, Adamu Tafida, Arsalaan Khan Yousafzai   +6 more
doaj   +1 more source

SE-stacking: Improving user purchase behavior prediction by information fusion and ensemble learning.

PLoS ONE, 2020
Online shopping behavior has the characteristics of rich granularity dimension and data sparsity and presents a challenging task in e-commerce. Previous studies on user behavior prediction did not seriously discuss feature selection and ensemble design ...
Jing Xu, Jie Wang, Ye Tian, Jiangpeng Yan, Xiu Li, Xin Gao   +5 more
doaj   +1 more source

A New Framework for Classifying University-Industry Collaboration Using Synthetic Minority Oversampling Technique and Stacking Ensemble

IEEE Access
University-industry collaboration has emerged as a critical driver of innovation and economic growth. However, predicting the outcomes of these collaborations remains methodologically challenging.
Uzapi Hange, Ezenwa Chike Nwanesi, Monhesea Obrey Patrick Bah   +2 more
doaj   +1 more source

XStacking : An effective and inherently explainable framework for stacked ensemble learning

Information Fusion
Ensemble Machine Learning (EML) techniques, especially stacking, have proven effective in boosting predictive performance by combining several base models. However, traditional stacked ensembles often face challenges in predictive effectiveness of the learning space and model interpretability, which limit their practical application.
Garouani, Moncef, Barhrhouj, Ayah, Teste, Olivier   +2 more
openaire   +2 more sources

Active Learning for Stacking and AdaBoost-Related Models

Stats
Ensemble learning (EL) has become an essential technique in machine learning that can significantly enhance the predictive performance of basic models, but it also comes with an increased cost of computation.
Qun Sui, Sujit K. Ghosh
doaj   +1 more source

An Ensemble-Based Approach for Detecting Clickbait in Indonesian Online Media

Jurnal Masyarakat Informatika
Clickbait headlines are widely used in online media to attract readers through exaggerated or misleading titles, potentially leading to user dissatisfaction and information overload. This study proposes a machine learning approach for detecting clickbait
Sandy Kurniawan, Adhe Setya Pramayoga, Yeva Fadhilah Ashari   +2 more
doaj   +1 more source

PhishNet 1.0: optuna-optimized stacking ensemble with Boruta-based feature selection for phishing URL detection

Scientific Reports
The objective of this research is to enhance phishing detection through ensemble learning integrated with well-structured metaheuristic algorithms. Various classifiers, including Logistic Regression, Nearest Neighbors, Support Vector Machine, Decision ...
Achin Jain, Shakir Khan, Kashish Koli, Harsh Taneja, Arvind Panwar, Thamer Alshammari, Essam Mohamed Obaid, Seid Mehammed Abdu   +7 more
doaj   +1 more source