Estimating and explaining model performance when both covariates and labels shift
Deployed machine learning (ML) models often encounter new user data that differs from
their training data. Therefore, estimating how well a given model might perform on the new …
their training data. Therefore, estimating how well a given model might perform on the new …
SoK: The impact of unlabelled data in cyberthreat detection
G Apruzzese, P Laskov… - 2022 IEEE 7th European …, 2022 - ieeexplore.ieee.org
Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD)
in the recent years. A substantial research effort has been invested in the development of …
in the recent years. A substantial research effort has been invested in the development of …
Two density-based sampling approaches for imbalanced and overlapping data
S Mayabadi, H Saadatfar - Knowledge-Based Systems, 2022 - Elsevier
An imbalanced dataset consists of a majority class and a minority class, where the former's
sample size is substantially larger than other classes. This difference disrupts the data …
sample size is substantially larger than other classes. This difference disrupts the data …
[HTML][HTML] Poverty classification using machine learning: the case of Jordan
The scope of this paper is focused on the multidimensional poverty problem in Jordan.
Household expenditure and income surveys provide data that are used for identifying and …
Household expenditure and income surveys provide data that are used for identifying and …
Machine learning model for screening thyroid stimulating hormone receptor agonists based on updated datasets and improved applicability domain metrics
W Liu, Z Wang, J Chen, W Tang… - Chemical Research in …, 2023 - ACS Publications
Machine learning (ML) models for screening endocrine-disrupting chemicals (EDCs), such
as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound …
as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound …
A semi-supervised resampling method for class-imbalanced learning
Z Jiang, L Zhao, Y Lu, Y Zhan, Q Mao - Expert Systems with Applications, 2023 - Elsevier
Clustering analysis is widely used as a pre-process to discover the data distribution for
resampling. Existing clustering-based resampling methods mostly run unsupervised …
resampling. Existing clustering-based resampling methods mostly run unsupervised …
[HTML][HTML] Overview of machine learning part 1: fundamentals and classic approaches
As health data and computer power become increasingly available, the main challenge is to
gain actionable insight from these data. Machine learning (ML) methods have proved to be a …
gain actionable insight from these data. Machine learning (ML) methods have proved to be a …
Privacy leakage of LoRaWAN smart parking occupancy sensors
Abstract Development of smart cities is enabled by its core concepts of smart and
sustainable mobility, where Low Power Wide Area Network (LPWAN) such as Long Range …
sustainable mobility, where Low Power Wide Area Network (LPWAN) such as Long Range …
On supervised class-imbalanced learning: An updated perspective and some key challenges
S Das, SS Mullick, I Zelinka - IEEE Transactions on Artificial …, 2022 - ieeexplore.ieee.org
The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …
traditional machine learning and the emerging deep learning research communities. A …
Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification
The diagnosis of Parkinson's disease (PD) is important in neurological pathology for
appropriate medical therapy. Algorithms based on decision tree induction (DTI) have been …
appropriate medical therapy. Algorithms based on decision tree induction (DTI) have been …