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 …
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 …
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 …
A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations
Abstract Machine learning is a field which studies how machines can alter and adapt their
behavior, improving their actions according to the information they are given. This field is …
behavior, improving their actions according to the information they are given. This field is …
A novel SMOTE-based resampling technique trough noise detection and the boosting procedure
Most of the classification methods assume that the numbers of class observations are
balanced. In such cases, models are predicted by giving biased weight to the the class with …
balanced. In such cases, models are predicted by giving biased weight to the the class with …
A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting
Over the last two decades, the diagnosis of bankruptcy firms has become extremely
important to business owners, banks, governments, securities investors, and economic …
important to business owners, banks, governments, securities investors, and economic …
Semantic and correlation disentangled graph convolutions for multilabel image recognition
Multilabel image recognition (MLR) aims to annotate an image with comprehensive labels
and suffers from object occlusion or small object sizes within images. Although the existing …
and suffers from object occlusion or small object sizes within images. Although the existing …
Multi-label sampling based on local label imbalance
Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label
learning methods. One efficient and flexible strategy to deal with this problem is to employ …
learning methods. One efficient and flexible strategy to deal with this problem is to employ …
On the dynamics of classification measures for imbalanced and streaming data
D Brzezinski, J Stefanowski… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
As each imbalanced classification problem comes with its own set of challenges, the
measure used to evaluate classifiers must be individually selected. To help researchers …
measure used to evaluate classifiers must be individually selected. To help researchers …
Label correlation guided borderline oversampling for imbalanced multi-label data learning
Multi-label data classification has received much attention due to its wide range of
application domains. Unfortunately, a class imbalance problem often occurs in multi-label …
application domains. Unfortunately, a class imbalance problem often occurs in multi-label …