A review of methods for imbalanced multi-label classification
Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …
classification where each data instance is associated with several labels simultaneously …
Tomek link and SMOTE approaches for machine fault classification with an imbalanced dataset
EF Swana, W Doorsamy, P Bokoro - Sensors, 2022 - mdpi.com
Data-driven methods have prominently featured in the progressive research and
development of modern condition monitoring systems for electrical machines. These …
development of modern condition monitoring systems for electrical machines. These …
Effective class-imbalance learning based on SMOTE and convolutional neural networks
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …
Towards class-imbalance aware multi-label learning
Multi-label learning deals with training examples each represented by a single instance
while associated with multiple class labels. Due to the exponential number of possible label …
while associated with multiple class labels. Due to the exponential number of possible label …
A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback–Leibler divergence
W Zhang, R Xie, Q Wang, Y Yang, J Li - Decision Support Systems, 2022 - Elsevier
The task of detecting fraudulent reviewers is of great importance to E-commerce platforms.
Existing research has invested much effort into developing comprehensive features and …
Existing research has invested much effort into developing comprehensive features and …
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 …
Multi-label borderline oversampling technique
Class imbalance problem commonly exists in multi-label classification (MLC) tasks. It has
non-negligible impacts on the classifier performance and has drawn extensive attention in …
non-negligible impacts on the classifier performance and has drawn extensive attention in …
多标签文本分类研究回顾与展望.
张文峰, 奚雪峰, 崔志明, 邹逸晨… - Journal of Computer …, 2023 - search.ebscohost.com
文本分类(TC) 是自然语言处理(NLP) 领域的重要基础任务, 多标签文本分类(MLTC) 是TC
的重要分支. 为了对多标签文本分类领域进行深入了解, 介绍了多标签文本分类的概念和流程 …
的重要分支. 为了对多标签文本分类领域进行深入了解, 介绍了多标签文本分类的概念和流程 …
A diversity and reliability-enhanced synthetic minority oversampling technique for multi-label learning
The class imbalance issue is generally intrinsic in multi-label datasets due to the fact that
they have a large number of labels and each sample is associated with only a few of them …
they have a large number of labels and each sample is associated with only a few of them …
Toward hierarchical classification of imbalanced data using random resampling algorithms
Although the class imbalance issue affects hierarchical datasets, the literature has few
studies that deal with it, especially when it comes to methods to pre-process the training …
studies that deal with it, especially when it comes to methods to pre-process the training …