Results 221 to 230 of about 35,702 (279)

EEFSA-SECM: an enhanced ensemble feature selection and stacking ensemble classifier to detect Parkinson's disease. [PDF]

Front Neurol
Rajput V, Maheswari N.
europepmc   +1 more source

Inter‐Model Feature Fusion for Robust Low‐Resource Speech Recognition

Applied AI Letters, Volume 7, Issue 2, June 2026.
Our Self‐Supervised Feature Fusion (SSF‐FT) method enhances low‐resource speech recognition by adaptively combining features from self‐supervised models trained with Contrastive, Predictive, and Reconstruction objectives. This attention‐weighted ensemble delivers robust performance, particularly in acoustically challenging conditions, extending current
Ussen Kimanuka, Ciira wa Maina, Osman Büyük   +2 more
wiley   +1 more source

Improving predictive reliability and automation of smart grids using the StarNet ensemble model. [PDF]

Sci Rep
Chhabra A, Singh SK, Kumar S, Singh M, Chauhan U, Arora S, Alhalabi W, Arya V, Alsulami BS, Hsu CH, Gupta BB.   +10 more
europepmc   +1 more source

A novel stacking ensemble model for predicting discharge coefficient of submerged multi parallel radial gates. [PDF]

Sci Rep
Abdelazim NM, Hosny M, Abdelhaleem FS, Elshenhab AM, Ibrahim A.   +4 more
europepmc   +1 more source

A hybrid machine learning approach for reliably predicting surface roughness in CNC turning operations. [PDF]

Sci Rep
Yurtkuran H, Demirtaş G, Alpsalaz F, Uzel H, Zaitsev I.   +4 more
europepmc   +1 more source

Stacking ensemble machine learning for predicting photodetector performance under varying illumination intensities. [PDF]

Sci Rep
Öter A, Berktaş Z, Ersöz B, Sağıroğlu Ş, Orhan E.   +4 more
europepmc   +1 more source

A hybrid stacked ensemble learning framework for multilabel text emotion detection. [PDF]

Sci Rep
Adamu H, Azmi Murad MA, Nasharuddin NA.
europepmc   +1 more source

An interpretability heart disease prediction model based on stacking ensemble with SHAP. [PDF]

Front Mol Biosci
Chen Y, Chong L, Bao Z, Wang S, Wang Y, Feng Y.   +5 more
europepmc   +1 more source

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Reduced ensemble size stacking [ensemble learning]

16th IEEE International Conference on Tools with Artificial Intelligence, 2005
We investigate an algorithmic extension to the technique of stacked regression that prunes the size of a homogeneous ensemble set based on a consideration of the accuracy and diversity of the set members. We show that the pruned ensemble set is as accurate on average over the data-sets tested as the nonpruned version, which provides benefits in terms ...
N. Rooney, D. Patterson, C. Nugent
openaire   +1 more source

Reranking for Stacking Ensemble Learning

2010
Ensemble learning refers to the methods that combine multiple models to improve the performance. Ensemble methods, such as stacking, have been intensively studied, and can bring slight performance improvement. However, there is no guarantee that a stacking algorithm outperforms all base classifiers.
Buzhou Tang, Qingcai Chen, Xuan Wang, Xiaolong Wang   +3 more
openaire   +1 more source