SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …
considered" de facto" standard in the framework of learning from imbalanced data. This is …
[HTML][HTML] Learning from imbalanced data: open challenges and future directions
B Krawczyk - Progress in artificial intelligence, 2016 - Springer
Despite more than two decades of continuous development learning from imbalanced data
is still a focus of intense research. Starting as a problem of skewed distributions of binary …
is still a focus of intense research. Starting as a problem of skewed distributions of binary …
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
Learning from class-imbalanced data continues to be a common and challenging problem in
supervised learning as standard classification algorithms are designed to handle balanced …
supervised learning as standard classification algorithms are designed to handle balanced …
AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning
Ensemble models achieve high accuracy by combining a number of base estimators and
can increase the reliability of machine learning compared to a single estimator. Additionally …
can increase the reliability of machine learning compared to a single estimator. Additionally …
The improved AdaBoost algorithms for imbalanced data classification
W Wang, D Sun - Information Sciences, 2021 - Elsevier
Class imbalance is one of the most popular and important issues in the domain of
classification. The AdaBoost algorithm is an effective solution for classification, but it still …
classification. The AdaBoost algorithm is an effective solution for classification, but it still …
A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification
Sentiment analysis is a critical task of extracting subjective information from online text
documents. Ensemble learning can be employed to obtain more robust classification …
documents. Ensemble learning can be employed to obtain more robust classification …
Biomedical text categorization based on ensemble pruning and optimized topic modelling
A Onan - Computational and Mathematical Methods in …, 2018 - Wiley Online Library
Text mining is an important research direction, which involves several fields, such as
information retrieval, information extraction, and text categorization. In this paper, we …
information retrieval, information extraction, and text categorization. In this paper, we …
An empirical comparison of techniques for the class imbalance problem in churn prediction
Class imbalance brings significant challenges to customer churn prediction. Many solutions
have been developed to address this issue. In this paper, we comprehensively compare the …
have been developed to address this issue. In this paper, we comprehensively compare the …
Machine learning based mobile malware detection using highly imbalanced network traffic
In recent years, the number and variety of malicious mobile apps have increased drastically,
especially on Android platform, which brings insurmountable challenges for malicious app …
especially on Android platform, which brings insurmountable challenges for malicious app …
Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting
Load forecasting implies directly in financial return and information for electrical systems
planning. A framework to build wavenet ensemble for short-term load forecasting is …
planning. A framework to build wavenet ensemble for short-term load forecasting is …