Results 111 to 120 of about 10,458 (157)

Improvement of Concrete Crack Segmentation Performance Using Stacking Ensemble Learning

Applied Sciences (Switzerland), 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
exaly   +3 more sources

A Stacking Heterogeneous Ensemble Learning Method for the Prediction of Building Construction Project Costs

Applied Sciences (Switzerland), 2022
The accurate cost estimation of a construction project in the early stage plays a very important role in successfully completing the project. In the initial stage of construction, when the information necessary to predict construction cost is ...
Haneul Lee, Seokheon Yun
exaly   +3 more sources

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A stacked ensemble machine learning approach for the prediction of diabetes

Journal of Diabetes & Metabolic Disorders, 2023
Diabetes has become a leading cause of mortality in both developed and developing countries, impacting a growing number of individuals worldwide. As the prevalence of the disease continues to rise, researchers have diligently worked towards developing accurate diabetes prediction models.
Khondokar Oliullah, Mahedi Hasan Rasel, Md. Manzurul Islam, Md. Reazul Islam, Md. Anwar Hussen Wadud, Md. Whaiduzzaman   +5 more
openaire   +3 more sources

A cross-entropy based stacking method in ensemble learning

Journal of Intelligent & Fuzzy Systems, 2020
Stacking is one of the major types of ensemble learning techniques in which a set of base classifiers contributes their outputs to the meta-level classifier, and the meta-level classifier combines them so as to produce more accurate classifications. In this paper, we propose a new stacking algorithm that defines the cross-entropy as the loss function ...
Weimin Ding, Shengli Wu 0001
openaire   +1 more source

A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models

Diagnostics, 2023
Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experience and inter-observer variability. This variability can cause minor lesions to be missed and prevent early diagnosis.
Esra Sivari, Erkan Bostanci, Mehmet Serdar Guzel   +2 more
exaly   +3 more sources

PreTP-Stack: Prediction of Therapeutic Peptide Based on the Stacked Ensemble Learning

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023
Therapeutic peptide prediction is critical for drug development and therapeutic therapy. Researchers have developed several computational methods to identify different therapeutic peptide types. However, most computational methods focus on identifying the specific type of therapeutic peptides and fail to accurately predict all types of therapeutic ...
Ke Yan 0003, Hongwu Lv, Jie Wen 0001, Yichen Guo, Yong Xu 0001, Bin Liu 0014   +5 more
openaire   +2 more sources

A Stacking Ensemble Learning Model for Mobile Traffic Prediction

2020 IEEE/CIC International Conference on Communications in China (ICCC), 2020
Mobile traffic prediction has been the foundation to enable effective network design and intelligent management. Machine learning (ML) methods have drawn extensive concern in this field. However, many existing methods fail to reach a satisfactory outcome, due to the fact that the performance of a single ML model is not always good.
Zhigang Li, Di Cai, Jialin Wang 0001, Jingchang Fu, Linlin Qin, Duomin Fu   +5 more
openaire   +1 more source

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 0002, Xiaolong Wang 0001   +3 more
openaire   +1 more source

A stacked ensemble learning method for customer lifetime value prediction

Kybernetes, 2023
PurposeWith the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV) has become crucial to sales managers. Predicting the CLV is a strategic weapon and competitive advantage in increasing profitability and identifying customers with more ...
Nader Asadi Ejgerdi, Mehrdad Kazerooni
openaire   +1 more source