Kwok Tai Chui^1,*, Varsha Arya¹, Brij B. Gupta^2,3,4,*, Miguel Torres-Ruiz⁵, Razaz Waheeb Attar⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068842 - 10 November 2025

Abstract Parkinson’s disease (PD) is a debilitating neurological disorder affecting over 10 million people worldwide. PD classification models using voice signals as input are common in the literature. It is believed that using deep learning algorithms further enhances performance; nevertheless, it is challenging due to the nature of small-scale and imbalanced PD datasets. This paper proposed a convolutional neural network-based deep support vector machine (CNN-DSVM) to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets. A customized kernel function reduces the impact… More >

I Made Putrama^1,2,*, Péter Martinek¹

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5699-5727, 2025, DOI:10.32604/cmc.2025.063465 - 19 May 2025

Abstract Imbalanced multiclass datasets pose challenges for machine learning algorithms. They often contain minority classes that are important for accurate predictions. However, when the data is sparsely distributed and overlaps with data points from other classes, it introduces noise. As a result, existing resampling methods may fail to preserve the original data patterns, further disrupting data quality and reducing model performance. This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling (NDESO), a hybrid method that integrates a data displacement strategy with a resampling technique to achieve data balance. It begins by computing the average distance of noisy… More >

Xuewen Mu^*, Bingcong Zhao

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2143-2159, 2025, DOI:10.32604/cmc.2025.060739 - 16 April 2025

Abstract When dealing with imbalanced datasets, the traditional support vector machine (SVM) tends to produce a classification hyperplane that is biased towards the majority class, which exhibits poor robustness. This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets. The proposed method first uses a biased second-order cone programming support vector machine (B-SOCP-SVM) to identify the support vectors (SVs) and non-support vectors (NSVs) in the imbalanced data. Then, it applies the synthetic minority over-sampling technique (SV-SMOTE) to oversample the support vectors of the minority class and uses the random under-sampling technique (NSV-RUS) multiple times More >

Yue Yang^1,2, Xiangyan Tang^2,3,*, Zhaowu Liu^2,3,*, Jieren Cheng^2,3, Haozhe Fang³, Cunyi Zhang³

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4389-4408, 2025, DOI:10.32604/cmc.2025.060357 - 06 March 2025

Abstract With the rapid development of Internet of Things technology, the sharp increase in network devices and their inherent security vulnerabilities present a stark contrast, bringing unprecedented challenges to the field of network security, especially in identifying malicious attacks. However, due to the uneven distribution of network traffic data, particularly the imbalance between attack traffic and normal traffic, as well as the imbalance between minority class attacks and majority class attacks, traditional machine learning detection algorithms have significant limitations when dealing with sparse network traffic data. To effectively tackle this challenge, we have designed a lightweight… More >

Yuewei Wu¹, Ruiling Fu¹, Tongtong Xing¹, Fulian Yin^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 751-775, 2025, DOI:10.32604/cmc.2024.057606 - 03 January 2025

Abstract In the Internet era, recommendation systems play a crucial role in helping users find relevant information from large datasets. Class imbalance is known to severely affect data quality, and therefore reduce the performance of recommendation systems. Due to the imbalance, machine learning algorithms tend to classify inputs into the positive (majority) class every time to achieve high prediction accuracy. Imbalance can be categorized such as by features and classes, but most studies consider only class imbalance. In this paper, we propose a recommendation system that can integrate multiple networks to adapt to a large number… More >

Dazhi E¹, Jiale Liu², Ming Zhang^1,*, Huiyuan Jiang², Keming Mao²

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3853-3880, 2024, DOI:10.32604/cmc.2024.057538 - 19 December 2024

Abstract Imbalance is a distinctive feature of many datasets, and how to make the dataset balanced become a hot topic in the machine learning field. The Synthetic Minority Oversampling Technique (SMOTE) is the classical method to solve this problem. Although much research has been conducted on SMOTE, there is still the problem of synthetic sample singularity. To solve the issues of class imbalance and diversity of generated samples, this paper proposes a hybrid resampling method for binary imbalanced data sets, RE-SMOTE, which is designed based on the improvements of two oversampling methods parameter-free SMOTE (PF-SMOTE) and… More >

Fahim Nasir¹, Abdulghani Ali Ahmed^1,*, Mehmet Sabir Kiraz¹, Iryna Yevseyeva¹, Mubarak Saif²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1703-1728, 2024, DOI:10.32604/cmc.2024.055192 - 15 October 2024

Abstract Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making. However, imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics, limiting their overall effectiveness. This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers (SLCs) and evaluates their performance in data-driven decision-making. The evaluation uses various metrics, with a particular focus on the Harmonic Mean Score (F-1 score) on an imbalanced real-world bank target marketing dataset. The findings indicate… More >

P. K. A. Chitra¹, S. Appavu alias Balamurugan², S. Geetha³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*, Keejun Han⁸

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1367-1385, 2024, DOI:10.32604/csse.2023.034373 - 13 September 2024

Abstract A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning. The main objective of this work is to create a novel framework for learning and classifying imbalanced multi-label data. This work proposes a framework of two phases. The imbalanced distribution of the multi-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1. Later, an adaptive weighted l₂₁ norm regularized (Elastic-net) multi-label logistic regression is used to predict unseen samples in phase 2. The proposed… More >

Congcong Ma^1,2, Jiaqi Mi¹, Wanlin Gao^1,2, Sha Tao^1,2,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4243-4261, 2024, DOI:10.32604/cmc.2024.054506 - 12 September 2024

Abstract Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes. This task is prevalent in practical scenarios such as industrial fault diagnosis, network intrusion detection, cancer detection, etc. In imbalanced classification tasks, the focus is typically on achieving high recognition accuracy for the minority class. However, due to the challenges presented by imbalanced multi-class datasets, such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries, existing methods often do not perform well in multi-class imbalanced data… More >

Jiqiang Chen, Ranran Han, Dongqing Zhang, Litao Ma^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5067-5082, 2024, DOI:10.32604/cmc.2024.051494 - 20 June 2024

Abstract Imbalanced datasets are common in practical applications, and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes. However, the creation of fuzzy rules typically depends on expert knowledge, which may not fully leverage the label information in training data and may be subjective. To address this issue, a novel fuzzy rule oversampling approach is developed based on the learning vector quantization (LVQ) algorithm. In this method, the label information of the training data is utilized to determine the antecedent… More >