Results 41 to 50 of about 35,527 (280)

Ensemble of Example-Dependent Cost-Sensitive Decision Trees [PDF]

, 2015
Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes.
Aouada, Djamila, Bahnsen, Alejandro Correa, Ottersten, Bjorn   +2 more
core   +2 more sources

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm

Frontiers in Oncology, 2022
Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs).
Quan-Hao He, Hao Tan, Fang-Tong Liao, Yi-Neng Zheng, Fa-Jin Lv, Qing Jiang, Ming-Zhao Xiao   +6 more
doaj   +1 more source

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

Applied Sciences, 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 ...
Uyeol Park, Yunho Kang, Haneul Lee, Seokheon Yun   +3 more
doaj   +1 more source

Bagging ensemble selection [PDF]

, 2011
Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems.
Pfahringer, Bernhard, Sun, Quan
core   +1 more source

Stacked Deep Learning Ensemble for Multiomics Cancer Type Classification: Development and Validation Study

JMIR Bioinformatics and Biotechnology
BackgroundCancer is one of the leading causes of disease burden globally, and early and accurate diagnosis is crucial for effective treatment. This study presents a deep learning–based model designed to classify 5 common types of cancer in Saudi Arabia ...
Amani Ameen, Nofe Alganmi, Nada Bajnaid
doaj   +1 more source

Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing

Remote Sensing, 2023
Crop chlorophyll content measuring plays a vital role in monitoring crop growth and optimizing agricultural inputs such as water and fertilizer. However, traditional methods for measuring chlorophyll content primarily rely on labor-intensive chemical ...
Weiguang Zhai, Changchun Li, Qian Cheng, Fan Ding, Zhen Chen   +4 more
doaj   +1 more source

Explainable stacking ensemble with feature tokenizer transformers for men’s diabetes prediction [PDF]

Journal of Men's Health
Diabetes is a leading global health concern, with millions of deaths linked to diabetes and related complications according to the World Health Organization (WHO). Early and accurate prediction is crucial for effective management.
Vinh Quang Tran, Younsung Choi, Haewon Byeon   +2 more
doaj   +1 more source

Large-Scale Multi-Label Learning with Incomplete Label Assignments [PDF]

, 2014
Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations.
Fan, Wei, Kong, Xiangnan, Li, Li-Jia, Wu, Hang, Wu, Zhaoming, Yu, Philip S., Zhang, Ruofei   +6 more
core   +2 more sources

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, Koray Acici, Tunc Asuroglu, Tulin Ercelebi Ayyildiz   +5 more
doaj   +1 more source

Evolving Non-linear Stacking Ensembles for Prediction of Go Player Attributes

, 2015
The paper presents an application of non-linear stacking ensembles for prediction of Go player attributes. An evolutionary algorithm is used to form a diverse ensemble of base learners, which are then aggregated by a stacking ensemble.
Moudřík, Josef, Neruda, Roman
core   +1 more source