Zobrazeno 1 - 10
of 333
pro vyhledávání: '"Positive-unlabeled learning"'
Publikováno v:
Geoenvironmental Disasters, Vol 11, Iss 1, Pp 1-15 (2024)
Abstract Introduction The Indian Himalayas' susceptibility to landslides, particularly as a location where climate change effects may be event catalysts, necessitates the development of dependable landslide susceptibility maps (LSM). Method This stud
Externí odkaz:
https://doaj.org/article/ba9b4769fe7e414a8c7ade3752b514ac
Publikováno v:
GIScience & Remote Sensing, Vol 61, Iss 1 (2024)
Recently, the positive unlabeled (PU) learning algorithms have proven highly effective in generating accurate landslide susceptibility maps. The algorithms categorize samples exclusively into positive samples (landslides) and unlabeled samples for tr
Externí odkaz:
https://doaj.org/article/d76e616e57534c499c865757a5906dee
Autor:
Praveen Kumar, Christophe G. Lambert
Publikováno v:
PeerJ Computer Science, Vol 10, p e2451 (2024)
Positive and unlabeled (PU) learning is a type of semi-supervised binary classification where the machine learning algorithm differentiates between a set of positive instances (labeled) and a set of both positive and negative instances (unlabeled). P
Externí odkaz:
https://doaj.org/article/721f96e05e9a471e99476370559e4c57
Autor:
Shiwei Xu, Margaret E. Ackerman
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-17 (2024)
Abstract Background Compared to traditional supervised machine learning approaches employing fully labeled samples, positive-unlabeled (PU) learning techniques aim to classify “unlabeled” samples based on a smaller proportion of known positive ex
Externí odkaz:
https://doaj.org/article/2a77ea4295814531885684129be5d940
Publikováno v:
In Knowledge-Based Systems 25 November 2024 304
Publikováno v:
In Journal of Controlled Release 10 February 2025 378:619-636
Publikováno v:
Entropy, Vol 26, Iss 5, p 403 (2024)
In a standard binary supervised classification task, the existence of both negative and positive samples in the training dataset are required to construct a classification model. However, this condition is not met in certain applications where only o
Externí odkaz:
https://doaj.org/article/a659d03b2dcb4c64bf011c74bb6aafa7
Autor:
Dino Ienco
Publikováno v:
IEEE Access, Vol 11, Pp 20877-20884 (2023)
Positive and unlabelled (PU) learning for multi-variate time series classification refers to build a binary classification model when only a small set of positive and a large set of unlabelled samples are accessible at training stage. Different from
Externí odkaz:
https://doaj.org/article/1e32ec9ae6354b7a93b2a3ea71c0e811
Autor:
Lihong Peng, Liangliang Huang, Geng Tian, Yan Wu, Guang Li, Jianying Cao, Peng Wang, Zejun Li, Lian Duan
Publikováno v:
Frontiers in Microbiology, Vol 14 (2023)
BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and disea
Externí odkaz:
https://doaj.org/article/fc5c41f245a24481a1c32de8dd44545f
Publikováno v:
Computers, Vol 13, Iss 2, p 49 (2024)
Identifying accidents in road black spots is crucial for improving road safety. Traditional methodologies, although insightful, often struggle with the complexities of imbalanced datasets, while machine learning (ML) techniques have shown promise, ou
Externí odkaz:
https://doaj.org/article/307ff1d119264f7d9f5a81392b0e9c64