Zobrazeno 1 - 10
of 243
pro vyhledávání: '"Xibei Yang"'
Publikováno v:
AIMS Mathematics, Vol 9, Iss 7, Pp 17504-17530 (2024)
Learning from imbalanced data is a challenging task in the machine learning field, as with this type of data, many traditional supervised learning algorithms tend to focus more on the majority class while damaging the interests of the minority class.
Externí odkaz:
https://doaj.org/article/23cf291005224d8d951cab92b08a252c
Publikováno v:
Electronic Research Archive, Vol 32, Iss 5, Pp 3038-3058 (2024)
Imbalanced data distribution and label correlation are two intrinsic characteristics of multi-label data. This occurs because in this type of data, instances associated with certain labels may be sparse, and some labels may be associated with others,
Externí odkaz:
https://doaj.org/article/e1f2e24019024786981b0cd1b6f9db2a
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 2, Pp 2626-2645 (2024)
Calculating single-source shortest paths (SSSPs) rapidly and precisely from weighted digraphs is a crucial problem in graph theory. As a mathematical model of processing uncertain tasks, rough sets theory (RST) has been proven to possess the ability
Externí odkaz:
https://doaj.org/article/7a1d33e8f66048478392eae7405e44ca
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 7, Pp 12772-12801 (2023)
There are approximately 2.2 billion people around the world with varying degrees of visual impairments. Among them, individuals with severe visual impairments predominantly rely on hearing and touch to gather external information. At present, there a
Externí odkaz:
https://doaj.org/article/c861e98c1f0f40329971a8f3cffd1450
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:
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
Publikováno v:
IEEE Access, Vol 11, Pp 113580-113592 (2023)
In the construction industry, it is common occurrence for head injuries caused by workers not wearing a helmet. However, the current models for detecting safety helmet either have insufficient detection accuracy or insufficient generalization ability
Externí odkaz:
https://doaj.org/article/d8d24c08724a47e2b9e5e202f27c28bc
Publikováno v:
IEEE Access, Vol 10, Pp 120578-120591 (2022)
This study proposes a novel incremental learning algorithm called distribution matching ensemble (DME) in context of adaptive weighted ensemble learning. In particular, DME estimates the distribution of each received data block by Gaussian mixture mo
Externí odkaz:
https://doaj.org/article/36266d99bc514d9b809a92304c578e56
Publikováno v:
Mathematics, Vol 11, Iss 8, p 1969 (2023)
Label-specific feature learning has become a hot topic as it induces classification models by accounting for the underlying features of each label. Compared with single-label annotations, multi-label annotations can describe samples from more compreh
Externí odkaz:
https://doaj.org/article/60954ff7ac7342e99a00812fa5be152c
Autor:
Pingxin Wang, Xibei Yang
Publikováno v:
IEEE Access, Vol 9, Pp 33944-33953 (2021)
Two-way clustering algorithms use one single set to represent a cluster, which cannot intuitively show the fringe objects of a cluster. Three-way clustering uses the core region and the fringe region to describe a cluster, which divide the universe i
Externí odkaz:
https://doaj.org/article/a435a87b6d7348aebd80abc85937d66e