A comprehensive survey on support vector machine classification: Applications, challenges and trends
J Cervantes, F Garcia-Lamont, L Rodríguez-Mazahua… - Neurocomputing, 2020 - Elsevier
In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …
machines (SVMs) and their application in several fields of science. SVMs are one of the …
A broad review on class imbalance learning techniques
S Rezvani, X Wang - Applied Soft Computing, 2023 - Elsevier
The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …
presence of asymmetrical class distribution. Due to the complex characteristics of …
Automatic speech recognition: a survey
Recently great strides have been made in the field of automatic speech recognition (ASR) by
using various deep learning techniques. In this study, we present a thorough comparison …
using various deep learning techniques. In this study, we present a thorough comparison …
A survey of predictive modeling on imbalanced domains
P Branco, L Torgo, RP Ribeiro - ACM computing surveys (CSUR), 2016 - dl.acm.org
Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
On the joint-effect of class imbalance and overlap: a critical review
MS Santos, PH Abreu, N Japkowicz… - Artificial Intelligence …, 2022 - Springer
Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …
other data intrinsic characteristics, among which class overlap stands out as one of the most …
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
V López, A Fernández, S García, V Palade… - Information sciences, 2013 - Elsevier
Training classifiers with datasets which suffer of imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …
important problem in data mining. This issue occurs when the number of examples …
Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring
Y Yuan, J Wei, H Huang, W Jiao, J Wang… - … Applications of Artificial …, 2023 - Elsevier
In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …
New imbalanced bearing fault diagnosis method based on Sample-characteristic Oversampling TechniquE (SCOTE) and multi-class LS-SVM
J Wei, H Huang, L Yao, Y Hu, Q Fan, D Huang - Applied Soft Computing, 2021 - Elsevier
In actual industrial production, the historical data sets used for bearing fault diagnosis are
generally limited and imbalanced and consist of multiple classes. These problems present …
generally limited and imbalanced and consist of multiple classes. These problems present …
Training and assessing classification rules with imbalanced data
The problem of modeling binary responses by using cross-sectional data has been
addressed with a number of satisfying solutions that draw on both parametric and …
addressed with a number of satisfying solutions that draw on both parametric and …
Incremental learning of concept drift from streaming imbalanced data
Learning in nonstationary environments, also known as learning concept drift, is concerned
with learning from data whose statistical characteristics change over time. Concept drift is …
with learning from data whose statistical characteristics change over time. Concept drift is …