Results 51 to 60 of about 65,886 (196)

Stacked LSTM Snapshot Ensembles for Time Series Forecasting [PDF]

, 2019
Ensembles of machine learning models have proven to improve the performance of prediction tasks in various domains. The additional computational costs for the performance increase are usually high since multiple models must be trained. Recently, snapshot ensembles (Huang et al.
Krstanovic, Sascha, Paulheim, Heiko
openaire   +2 more sources

Improved stacking ensemble learning based on feature selection to accurately predict warfarin dose

Frontiers in Cardiovascular Medicine
BackgroundWith the rapid development of artificial intelligence, prediction of warfarin dose via machine learning has received more and more attention.
Mingyuan Wang, Mingyuan Wang, Yiyi Qian, Yaodong Yang, Haobin Chen, Wei-Feng Rao   +5 more
doaj   +1 more source

Demand Forecasting of Online Car-Hailing With Stacking Ensemble Learning Approach and Large-Scale Datasets

IEEE Access, 2020
With the rapid development and convenient service of online car-hailing, it has gradually become the preferred choice for people to travel. Accurate forecasting of car-hailing trip demand not only enables the drivers and companies to dispatch the ...
Yuming Jin, Xiaofei Ye, Qiming Ye, Tao Wang, Jun Cheng, Xingchen Yan   +5 more
doaj   +1 more source

Day-Ahead Forecast of Photovoltaic Power Based on a Novel Stacking Ensemble Method

IEEE Access, 2023
Accurate prediction of photovoltaic (PV) power is the prerequisite for the safe and stable operation of the power grid with high penetration of PV. Despite various machine learning models for forecasting PV power have been developed, their accuracies are
Luyao Liu, Qie Sun, Ronald Wennersten, Zhigang Chen   +3 more
doaj   +1 more source

GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

, 2019
In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection.
Gong, Mackenzie, Liu, Yan, Liu, Yang, Peng, Siyao, Yu, Yue, Zeldes, Amir, Zhu, Yilun   +6 more
core   +1 more source

Lifestyle data-based multiclass obesity prediction with interpretable ensemble models incorporating SHAP and LIME analysis

Scientific Reports
Obesity is a major public health concern. Predicting obesity risk from lifestyle data can guide targeted interventions, but current models remain limited.
Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Zhongming Zhao   +2 more
doaj   +1 more source

Enhancing credit card fraud detection with a stacking-based hybrid machine learning approach [PDF]

PeerJ Computer Science
The swift progression of technology has increased the complexity of cyber fraud, posing an escalating challenge for the banking sector to reliably and efficiently identify fraudulent credit card transactions.
Eyad Abdel Latif Marazqah Btoush, Xujuan Zhou, Raj Gururajan, Ka Ching Chan, Omar Alsodi   +4 more
doaj   +2 more sources

Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach

Future Internet, 2022
Forecasting the risk factor of the financial frontier markets has always been a very challenging task. Unlike an emerging market, a frontier market has a missing parameter named “volatility”, which indicates the market’s risk and as a result of the ...
Mst. Shapna Akter, Hossain Shahriar, Reaz Chowdhury, M. R. C. Mahdy   +3 more
doaj   +1 more source

Hyperuniformity Order Metric of Barlow Packings

, 2018
The concept of hyperuniformity has been a useful tool in the study of large-scale density fluctuations in systems ranging across the natural and mathematical sciences.
Middlemas, Timothy M., Stillinger, Frank H., Torquato, Salvatore   +2 more
core   +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