For class-imbalance problems, traditional supervised learning algorithms tend to favor
majority instances (also called negative instances). Therefore, it is difficult for them to …

Abstract The Synthetic Minority Oversampling Technique (SMOTE) method is the baseline
for solving unbalanced data problems. The working concept of the SMOTE method is to …

The class imbalance problem is one of the difficult factors affecting the performance of
traditional classifiers. The oversampling technique is the most common way to solve the …

Sentiment analysis is natural language processing (NLP) that uses text analysis to
recognize and extract opinions in text. Analysis is used to convert unstructured information …

Data complexity for class-imbalance problems is a hot topic in the field of data mining.
Classical data complexity measures use techniques such as kNN, which computes the …

Data are an essential component for building machine learning models. Linearly separable
high-quality data are conducive to building efficient classification models. However, the …

The performance of predictive models in software defect prediction is vulnerable to
redundant features. Feature selection methods prove effective in reducing the spatial …

In the era of big data, the complexity of data is increasing. Problems such as data imbalance
and class overlap pose challenges to traditional classifiers. Meanwhile, the importance of …

In this work, we introduce a novel oversampling technique, the theory of inheritance and
Gower distance-based oversampling (TIGO) method, designed to address class imbalance …

Imbalanced data is an important issues and challenges faced in data classification. This will
lead to poor performance of binary classifiers, this is due to bias in classification in favour of …