Results 11 to 20 of about 65,886 (196)

Stacking Ensemble Technique for Classifying Breast Cancer [PDF]

Healthcare Informatics Research, 2019
Breast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast cancer are very important. As a supporting tool for classifying breast cancer, we tried to identify the best meta-learner model in a stacking ensemble when ...
Hyunjin Kwon, Jinhyeok Park, Youngho Lee
openaire   +3 more sources

Multi-layer stacking ensemble learners for low footprint network intrusion detection

Complex & Intelligent Systems, 2022
Machine learning has become the standard solution to problems in many areas, such as image recognition, natural language processing, and spam detection.
Saeed Shafieian, Mohammad Zulkernine
doaj   +1 more source

An R package for ensemble learning stacking

Bioinformatics Advances, 2023
Abstract Summary Supervised learning is widely used in biology for prediction, and ensemble learning, including stacking, is a promising technique for increasing and stabilizing the prediction accuracy. In this study, we developed an R package for stacking.
Taichi Nukui, Akio Onogi
openaire   +2 more sources

Improvement of Concrete Crack Segmentation Performance Using Stacking Ensemble Learning

Applied Sciences, 2023
Signs of functional loss due to the deterioration of structures are primarily identified from cracks occurring on the surface of structures, and continuous monitoring of structural cracks is essential for socially important structures.
Taehee Lee, Jung-Ho Kim, Sung-Jin Lee, Seung-Ki Ryu, Bong-Chul Joo   +4 more
doaj   +1 more source

A Stacking Ensemble Prediction Model for the Occurrences of Major Adverse Cardiovascular Events in Patients With Acute Coronary Syndrome on Imbalanced Data

IEEE Access, 2021
The major adverse cardiovascular events (MACE) often occur with high morbidity and mortality globally. It is very important to predict the MACE occurrences accurately in patients with acute coronary syndrome (ACS).
Huilin Zheng, Syed Waseem Abbas Sherazi, Jong Yun Lee   +2 more
doaj   +1 more source

Multiple Imputation Ensembles (MIE) for dealing with missing data [PDF]

, 2020
Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation ...
A Farhangfar, AM Sefidian, B Schölkopf, C Cortes, CT Tran, DA Newman, DB Rubin, DB Rubin, DH Wolpert, EL Silva-Ramírez, GE Batista, GJ van der Heijden, H Gao, IH Witten, J Demšar, J Honaker, J Honaker, J Scheffer, JA Sterne, JL Schafer, JL Schafer, JR Quinlan, K Abayomi, KM Ting, L Breiman, L Breiman, L Rokach, M Fichman, M Khalilia, M Spratt, MA Klebanoff, MJ Azur, NJ Horton, PJ García-Laencina, PJ Kelly, PN Tan, RJ Little, S García, S Van Buuren, S Van Buuren, SS Chae, SS Choi, U Garciarena, V Vapnik, X Chen, Y Dong, Y Freund, Y He, Z Che, Z Liu   +49 more
core   +1 more source

A Novel Drug-Drug Indicator Dataset and Ensemble Stacking Model for Detection and Classification of Drug-Drug Interaction Indicators

IEEE Access, 2023
Drug-drug interaction (DDI) is a significant public health issue that accounts for 30% of unanticipated clinically hazardous medication events. The past decade has seen an evolution in informatics-based research for DDI signal identification. This
Sidra Abbas, Gabriel Avelino Sampedro, Mideth Abisado, Ahmad S. Almadhor, Tai-Hoon Kim, Monji Mohamed Zaidi   +5 more
doaj   +1 more source

Injury Severity Prediction From Two-Vehicle Crash Mechanisms With Machine Learning and Ensemble Models

IEEE Open Journal of Intelligent Transportation Systems, 2020
Machine learning algorithms aim to improve the power of predictors over conventional regression models. This study aims to tap the predictive potential of crash mechanism-related variables using ensemble machine learning models.
Ang Ji, David Levinson
doaj   +1 more source

Bagging ensemble selection for regression [PDF]

, 2012
Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have
D.H. Wolpert, E. Bauer, J. Demšar, J.H. Friedman, J.H. Friedman, L. Breiman, L. Rokach, Q. Sun, R. Bryll, Z.-H. Zhou, Z.H. Zhou   +10 more
core   +1 more source

Combining high resolution input and stacking ensemble machine learning algorithms for developing robust groundwater potentiality models in Bisha watershed, Saudi Arabia

Applied Water Science, 2022
The present research aims to build a unique ensemble model based on a high-resolution groundwater potentiality model (GPM) by merging the random forest (RF) meta classifier-based stacking ensemble machine learning method with high-resolution groundwater ...
Javed Mallick, Swapan Talukdar, Mohd. Ahmed   +2 more
doaj   +1 more source