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Imbalanced data classification problem is a prevalent concern within the realms of machine learning and data mining. However, conventional methods primarily concentrate on between-class imbalance, ignoring noisy, overlap and within-class issues. ...

Classification performance often deteriorates when machine learning algorithms are trained on imbalanced data. Although oversampling methods have been successfully employed to address imbalanced data, existing approaches have limitations such as ...

• We proposes feature information aggregation oversampling for imbalanced data classification (FIAO).
• The FIAO proposed in this study can effectively improve the performance of the classifier.
• This study uses a series of feature ...

• The method can address within-class imbalance, small sample sizes and small disjuncts.
• The oversampling simultaneously considers majority and minority class distribution.
• The introduced randomness and cut-off threshold can avoid ...

The imbalanced data classification has gained popularity in machine learning research domain due to its prevalence in numerous applications and its difficulty. However, the majority of contemporary work primarily focuses on addressing between-...

Imbalanced data is prevalent in various fields, including disease diagnosis. Effective imbalanced learning methods are crucial for improving supervised learning algorithms. Existing preprocessing methods of imbalanced learning still suffer from ...$$

Detecting fraudulent activities is a major worry for businesses and financial organizations because they can result in significant financial losses and reputational harm. Traditional fraud detection a method frequently depend on present rules and ...

The fuzzy c-means (FCM) clustering algorithm requires the pre-definition of the number of clusters. When used for imbalanced datasets, FCM tends to equalize the sizes of clusters and thus produces bad clustering results. Although clustering ...

• We propose a new clustering validity index that is effective for unbalanced data sets with multiple clusters.
• We design a merging algorithm that is able to produce majority clusters of high quality.
• This new CVI performs better ...

Nowadays, many classification algorithms have been applied to various industries to help them work out their problems met in real-life scenarios. However, in many binary classification tasks, samples in the minority class only make up a small ...

• Introduce a scoring system based on anomaly detection to the resampling strategy.
• The proposed weighting functions are intuitive and easy to understand.
• Propose an efficient ensemble learning framework.
• Excellent performance on ...

In recent years, the prevalence of imbalanced datasets has posed significant challenges to traditional machine learning models. This imbalance is especially pronounced in fields such as spam detection, where malicious or unwanted messages are ...

Ordinal classification of imbalanced datasets is a challenging problem that occurs in many real-world applications. The main challenge is to simultaneously consider the classes ordering and imbalanced distribution. Although the classic synthetic ...

Oversampling method is used to solve the class imbalanced issues. Some existing oversampling methods do not well remove noisy samples and avoid synthesizing noisy samples. Therefore, we propose a new oversampling approach based differential ...

• SS_DEBOHID improves diversity of synthesized samples and reduces noisy samples.
• SS_DEBOHID uses the basic strategy of DE to generate samples in the safe area.
• SS_DEBOHID is better than 10 methods on 43 highly imbalanced datasets.

The Internet is a crucial way to share information in both personal and professional areas. Sentiment analysis attracts great interest in marketing, research, and business today. The instability faced by imbalanced datasets on sentiment analysis ...

• A method is presented to address the class-imbalance and class-overlap problems.
• The proposed method is based on a metaheuristic-based under-sampling approach.
• The under-sampling problem is mapped into an optimization problem.
• ...

The problem of imbalanced class distribution in real-world datasets severely impairs the performance of classification algorithms. The learning task becomes more complicated and challenging when there is also the class-overlap problem in ...

Learning from imbalanced datasets is a nontrivial task for supervised learning community. Traditional classifiers may have difficulties to learn the concept related to the minority class when addressing imbalanced classification and ...

• A self-adaptive oversampling method based on minority data complexity is presented.

Imbalanced class distribution is a frequent and problematic issue in the domains of data engineering and machine learning. Traditional classification algorithms or machine learning models frequently fail, in this difficult situation, to provide ...

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• Imbalanced datasets pose common and important problems in many fields.
• A novel distance-based mega-trend-diffusion Siamese Network (DB-MTD-SN) oversampling approach is developed in this paper.
• The proposed method’s efficacy is ...

The class imbalance learning problem is an important topic that has attracted considerable attention in machine learning and data mining. The most common method of addressing imbalanced datasets is the synthetic minority oversampling technique (...

The paper focuses on methods and algorithms for oversampling two-classes imbalanced datasets. We propose a taxonomy for oversampling approaches and review state-of-the-art algorithms. The paper discusses also some strengths and weaknesses of the ...

Class imbalance learning is one of the most important topics in the field of machine learning and data mining, and the Synthetic Minority Oversampling Techniques (SMOTE) is the common method to handle this issue. The main shortcomings ...

The challenge of information overload in the legal domain increases every day. The COLIEE competition has created four challenges which are intended to encourage the development of systems and methods to alleviate some of that pressure: a case law ...

Software defect prediction (SDP) is essential to analyze and identify defects present in a software model in early stages of software development. The identification of these defects and their early removal provides cost-efficient software. ...

• A novel SVDD boundary-based weighted oversampling approach is presented.
• The ...

Imbalanced dataset classification issue poses a major challenge on machine learning domain. Traditional supervised learning algorithms usually bias towards the majority class when handling imbalanced datasets, thus leading to poor ...