Results 21 to 30 of about 35,527 (280)

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

Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles

Applied Sciences, 2022
Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers.
Amal Alazba, Hamoud Aljamaan
doaj   +1 more source

Stacking ensemble learning for optical music recognition

Bulletin of Electrical Engineering and Informatics, 2023
The development of music culture has resulted in a problem called optical music recognition (OMR). OMR is a task in computer vision that explores the algorithms and models to recognize musical notation. This study proposed the stacking ensemble learning model to complete the OMR task using the common western musical notation (CWMN) musical notation ...
Francisco Calvin Arnel Ferano, Amalia Zahra, Gede Putra Kusuma   +2 more
openaire   +1 more source

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

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

Application of tree based enhanced stacking ensemble learning

SSRN Electronic Journal, 2022
Abstract Since it is difficult for machine learning algorithms, such as XGBoost, LightGBM, to extract feature interaction effectively and deep learning algorithms have high time complexity, a tree enhanced stacking integrated model is proposed in this paper, based on tree model feature generation theory and convolutional neural network(CNN ...
Xiaofei Xu, Wu Yonghong
openaire   +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

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

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

A comprehensive evaluation of ensemble learning methods and decision trees for predicting trauma patient discharge status using real-world data [PDF]

Archives of Trauma Research, 2023
Background: Trauma registries collect and document data about the acute injury care in hospitals. The goal of trauma care systems is to reduce injury occurrence and enhance trauma patient survival rates.
Zahra Kohzadi, Ali Mohammad Nickfarjam, Leila Shokrizadeh Arani, Zeinab Kohzadi, Mehrdad Mahdian   +4 more
doaj   +1 more source