A review on solution to class imbalance problem: Undersampling approaches
The classification task carries a significant role in the field of effective data mining and
numerous classification models are proposed over the years to carry out the job. However …
numerous classification models are proposed over the years to carry out the job. However …
Emotions matter: A systematic review and meta-analysis of the detection and classification of students' emotions in stem during online learning
In recent years, the rapid growth of online learning has highlighted the need for effective
methods to monitor and improve student experiences. Emotions play a crucial role in …
methods to monitor and improve student experiences. Emotions play a crucial role in …
Redundancy-driven modified Tomek-link based undersampling: A solution to class imbalance
D Devi, B Purkayastha - Pattern Recognition Letters, 2017 - Elsevier
Class imbalance can be defined as a span among data mining, machine learning and
pattern recognition domains that provides to learn from a data-space having unequal class …
pattern recognition domains that provides to learn from a data-space having unequal class …
[HTML][HTML] Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques
A procedure to recognize individual discontinuities in rock mass from measurement while
drilling (MWD) technology is developed, using the binary pattern of structural rock …
drilling (MWD) technology is developed, using the binary pattern of structural rock …
An overlap-sensitive margin classifier for imbalanced and overlapping data
HK Lee, SB Kim - Expert Systems with Applications, 2018 - Elsevier
Classification is an important task in various areas. In many real-world applications, class
imbalance and overlapping problems have been reported as major issues in the application …
imbalance and overlapping problems have been reported as major issues in the application …
K-nearest neighbors rule combining prototype selection and local feature weighting for classification
X Zhang, H Xiao, R Gao, H Zhang, Y Wang - Knowledge-Based Systems, 2022 - Elsevier
Abstract K-Nearest Neighbors (KNN) rule is a simple yet powerful classification technique in
machine learning. Nevertheless, it suffers from some drawbacks such as high memory …
machine learning. Nevertheless, it suffers from some drawbacks such as high memory …
Learning in presence of class imbalance and class overlapping by using one-class SVM and undersampling technique
The class imbalance problem engraves the traditional learning models by degrading
performance and yielding erroneous outcomes. It is the scenario where one of the class …
performance and yielding erroneous outcomes. It is the scenario where one of the class …
A comprehensive investigation of the impact of class overlap on software defect prediction
Software Defect Prediction (SDP) is one of the most vital and cost-efficient operations to
ensure the software quality. However, there exists the phenomenon of class overlap in the …
ensure the software quality. However, there exists the phenomenon of class overlap in the …
A boosting-aided adaptive cluster-based undersampling approach for treatment of class imbalance problem
The subject of a class imbalance is a well-investigated topic which addresses performance
degradation of standard learning models due to uneven distribution of classes in a …
degradation of standard learning models due to uneven distribution of classes in a …
Predicting the helpfulness of online customer reviews: The role of title features
M Akbarabadi, M Hosseini - International Journal of Market …, 2020 - journals.sagepub.com
Nowadays, many people refer to online customer reviews that are available on most
shopping websites to make a better purchase decision. An automated review helpfulness …
shopping websites to make a better purchase decision. An automated review helpfulness …