Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Liang Sian Lin"'
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 10, Pp 17672-17701 (2023)
To handle imbalanced datasets in machine learning or deep learning models, some studies suggest sampling techniques to generate virtual examples of minority classes to improve the models' prediction accuracy. However, for kernel-based support vector
Externí odkaz:
https://doaj.org/article/efe284450da34588952261493e606225
Publikováno v:
Mathematical Biosciences and Engineering, Vol 19, Iss 6, Pp 6204-6233 (2022)
In the medical field, researchers are often unable to obtain the sufficient samples in a short period of time necessary to build a stable data-driven forecasting model used to classify a new disease. To address the problem of small data learning, man
Externí odkaz:
https://doaj.org/article/f97f4d61fc4c4014ba2831a219b32dac
Publikováno v:
Symmetry, Vol 14, Iss 2, p 339 (2022)
Generative adversarial networks are known as being capable of outputting data that can imitate the input well. This characteristic has led the previous research to propose the WGAN_MTD model, which joins the common version of Generative Adversarial N
Externí odkaz:
https://doaj.org/article/fa0b7ce1acb74c28a99b3cd4dd2f0a2b
Publikováno v:
Entropy, Vol 24, Iss 3, p 322 (2022)
Oversampling is the most popular data preprocessing technique. It makes traditional classifiers available for learning from imbalanced data. Through an overall review of oversampling techniques (oversamplers), we find that some of them can be regarde
Externí odkaz:
https://doaj.org/article/75fc0f39d2d2468d8c3df3ae120df61d
Publikováno v:
Machine Learning and Artificial Intelligence ISBN: 9781643683560
A large number of sensors based on Internet of Things (IoT) technology are now widely deployed in artificial intelligence, health care monitoring, air quality monitoring, and other fields. The sensors require high power consumption for real-time moni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9f44b4c28212abcd8564f4caf806ccfb
https://doi.org/10.3233/faia220434
https://doi.org/10.3233/faia220434
Publikováno v:
Applied Soft Computing. 143:110406
Publikováno v:
Mathematical Biosciences & Engineering; 2023, Vol. 20 Issue 10, p17672-17701, 30p
Publikováno v:
Decision Support Systems. :113996
Publikováno v:
PLoS ONE, Vol 12, Iss 8, p e0181853 (2017)
It is difficult for learning models to achieve high classification performances with imbalanced data sets, because with imbalanced data sets, when one of the classes is much larger than the others, most machine learning and data mining classifiers ar
Externí odkaz:
https://doaj.org/article/165a60ccb7b044f59aec23a60f7c97e8
Publikováno v:
Soft Computing. 23:11883-11900
A small dataset that contains very few samples, a maximum of thirty as defined in traditional normal distribution statistics, often makes it difficult for learning algorithms to make precise predictions. In past studies, many virtual sample generatio