Zobrazeno 1 - 10
of 690
pro vyhledávání: '"imbalance learning"'
Publikováno v:
Materials & Design, Vol 246, Iss , Pp 113310- (2024)
Predicting phase formation is crucial in novel high-entropy alloys (HEAs) design. Herein, machine learning and imbalance learning algorithms were combined together to improve the phase prediction of HEAs. In this work, an extensive database by collec
Externí odkaz:
https://doaj.org/article/650d18316b824a9c806934f80349cc80
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 35, Iss 4, Pp 36-49 (2023)
Cost-sensitive learning is a popular paradigm to address class-imbalance learning (CIL) problem. Traditional cost-sensitive learning approaches always solve CIL problem by assigning a constant higher training error penalty for all minority instances
Externí odkaz:
https://doaj.org/article/214d332866eb4a0197acdc86b80fb079
Publikováno v:
GIScience & Remote Sensing, Vol 60, Iss 1 (2023)
Uncertainty is a common problem in spatial modeling and geographical information systems (GIS). Furthermore, urban gain modeling (UGM) contains various dimensions and components of uncertainties. Data sampling is important in UGM, and may cause the r
Externí odkaz:
https://doaj.org/article/689502e8b8914719b28bdedb9bab19e9
Publikováno v:
Electronic Research Archive, Vol 31, Iss 5, Pp 2501-2518 (2023)
Class imbalance learning (CIL), which aims to addressing the performance degradation problem of traditional supervised learning algorithms in the scenarios of skewed data distribution, has become one of research hotspots in fields of machine learning
Externí odkaz:
https://doaj.org/article/03fa48d68a4e442d813ec619ed10eaed
Autor:
Xiaomeng An, Sen Xu
Publikováno v:
Electronic Research Archive, Vol 31, Iss 5, Pp 2733-2757 (2023)
Learning from imbalanced data is a challenging task, as with this type of data, most conventional supervised learning algorithms tend to favor the majority class, which has significantly more instances than the other classes. Ensemble learning is a r
Externí odkaz:
https://doaj.org/article/2822a593c1dd47aaa9e99d5ab06e86c7
Publikováno v:
IEEE Access, Vol 11, Pp 136666-136679 (2023)
Imbalanced learning jeopardizes the accuracy of traditional classification models, particularly for what concerns the minority class, which is often the class of interest. This paper addresses the issue of imbalanced learning in credit card fraud det
Externí odkaz:
https://doaj.org/article/9ec45e0a05af4872a6d8c7d6314e0594
Publikováno v:
IEEE Access, Vol 11, Pp 54839-54848 (2023)
Image classification techniques have succeeded greatly on various large-scale visual datasets using deep convolution neural networks. However, previous deep models usually suffer severe performance degradation in highly skewed datasets, which restric
Externí odkaz:
https://doaj.org/article/672e9c84af2d47cb89b6d216485fbf14
Akademický článek
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Publikováno v:
Tongxin xuebao, Vol 43, Pp 225-232 (2022)
Aiming at the problem that in the field of fraud detection, imbalance labels and lack of necessary connections between fraud nodes, resulting in fraud detection tasks not conforming to the hypothesis of homogeneity of graph neural networks, multi-vie
Externí odkaz:
https://doaj.org/article/bb4e51c56a094ebd87ccf6a5d8d31c65
Akademický článek
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