Results 1 to 10 of about 10,458 (157)

A Stacking Ensemble Learning Framework for Genomic Prediction. [PDF]

Front Genet, 2021
Abstract Background: Machine learning (ML) is perhaps the most useful for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) was unsatisfactory in existing research.
Liang M, Chang T, An B, Duan X, Du L, Wang X, Miao J, Xu L, Gao X, Zhang L, Li J, Gao H.   +11 more
europepmc   +6 more sources

StackNAFLD: An Accurate Stacking Ensemble Learning Targeting NAFLD Treatment. [PDF]

ACS Omega
Nonalcoholic fatty liver disease (NAFLD) is a slow-progressing yet complex disease with multiple pathophysiological mechanisms that make it challenging to treat. In this study, we developed a machine learning (ML)-based stacking ensemble model to predict molecules that could inhibit NAFLD progression utilizing data from animal experiments.
Intan AEK, Jarukamjorn K, Srisongkram T.
europepmc   +3 more sources

A stacking ensemble of deep learning models for IoT intrusion detection

Knowledge-Based Systems, 2023
The number of Internet of Things (IoT) devices has increased considerably in the past few years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a consequence, the prompt detection of attacks in IoT environments through the use of Intrusion Detection Systems (IDS) has become essential.
Riccardo Lazzarini, Huaglory Tianfield, Vassilis Charissis   +2 more
exaly   +3 more sources

Classification of Sleeping Position Using Enhanced Stacking Ensemble Learning. [PDF]

Entropy (Basel)
Sleep position recognition plays a crucial role in enhancing individual sleep quality and addressing sleep-related disorders. However, the conventional non-invasive technology for recognizing sleep positions tends to be limited in its widespread application due to high production and computing costs. To address this issue, an enhanced stacking model is
Xu X, Mo Q, Wang Z, Zhao Y, Li C.
europepmc   +4 more sources

Financial Distress Prediction with Stacking Ensemble Learning

IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2022
Previous studies have used financial ratios extensively to build their predictive model of financial distress. The Altman ratio is the most often used to predict, especially in academic studies. However, the Altman ratio is highly dependent on the validity of the data in financial statements, so other variables are needed to assess the possibility of ...
Hadi, Muhammad Fadhlil, Liang, De-Ron, Priyambodo, Tri Kuntoro, SN, Azhari   +3 more
openaire   +4 more sources

MFDroid: A Stacking Ensemble Learning Framework for Android Malware Detection. [PDF]

Sensors (Basel), 2022
As Android is a popular a mobile operating system, Android malware is on the rise, which poses a great threat to user privacy and security. Considering the poor detection effects of the single feature selection algorithm and the low detection efficiency of traditional machine learning methods, we propose an Android malware detection framework based on ...
Wang X, Zhang L, Zhao K, Ding X, Yu M.
europepmc   +4 more sources

Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee. [PDF]

Front Plant Sci
Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection.
Nascimento M, Nascimento ACC, Azevedo CF, de Oliveira ACB, Caixeta ET, Jarquin D.   +5 more
europepmc   +4 more sources

Stacked Ensemble Machine Learning for Range-Separation Parameters [PDF]

The Journal of Physical Chemistry Letters, 2021
Density functional theory-based high-throughput materials and drug discovery has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the nonempirical but expensive optimally tuned range-separated hybrid (OT-RSH) functionals were developed. An OT-
Cheng-Wei Ju, Ethan J. French, Nadav Geva, Alexander W. Kohn, Zhou Lin   +4 more
openaire   +3 more sources

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

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