Results 11 to 20 of about 6,303,129 (314)
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
IEEE Access, 2022 Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models.
Ibomoiye Domor Mienye, Yanxia Sun
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Domain Adaptive Ensemble Learning [PDF]
IEEE Transactions on Image Processing, 2021 The problem of generalizing deep neural networks from multiple source domains to a target one is studied under two settings: When unlabeled target data is available, it is a multi-source unsupervised domain adaptation (UDA) problem, otherwise a domain generalization (DG) problem.
Kaiyang Zhou +3 more
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Bioengineering, 2023 Ultrasound (US) is often used to diagnose liver masses. Ensemble learning has recently been commonly used for image classification, but its detailed methods are not fully optimized.
Norio Nakata, Tsuyoshi Siina
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A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation [PDF]
Expert systems with applications, 2023 Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than the other. Ensemble learning combines multiple models to obtain a robust model and has been prominently used with data ...
A. Khan +2 more
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A Unified Theory of Diversity in Ensemble Learning [PDF]
Journal of machine learning research, 2023 We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios. This challenge has been referred to as the holy grail of ensemble learning, an open research issue for over 30 years.
Danny Wood +5 more
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Ensemble Learning for Disease Prediction: A Review
Healthcare, 2023 Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single ...
P. Mahajan +3 more
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Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning [PDF]
IEEE Internet of Things Journal, 2022 Specific emitter identification (SEI) is a highly potential technology for physical-layer authentication that is one of the most critical supplements for the upper-layer authentication.
Yu Wang +5 more
semanticscholar +1 more source
A Forest Fire Detection System Based on Ensemble Learning
Forests, 2021 Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios.
Renjie Xu +4 more
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A diabetes prediction model based on Boruta feature selection and ensemble learning
BMC Bioinformatics, 2023 Background and objective As a common chronic disease, diabetes is called the “second killer” among modern diseases. Currently, there is no medical cure for diabetes. We can only rely on medication for auxiliary treatment.
Hongfang Zhou, Yinbo Xin, Suli Li
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