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Software defect prediction (SDP) aims to distinguish between defective and nondefective instances, but the imbalance between these two classes often leads to reduced prediction performance. Conventional SDP approaches use oversampling techniques,...

A novel mutation‐based data augmentation method is proposed, in which data are increased at the code level while preserving its semantic features. The method utilizes the mutation operator for generating mutants to mutate against nondefective instances ...

In practical applications of machine learning, the class distribution of the collected training set is usually imbalanced, i.e., there is a large difference among the sizes of different classes. The class imbalance problem often hinders the ...

Rumor detection is to identify and mitigate potentially damaging falsehoods, thereby shielding the public from misleading information. However, existing methods fall short of tackling class imbalance, meaning rumor is less common than true messages, as ...

Code smell is a software characteristic that indicates bad symptoms in code design which causes problems related to software quality. The severity of code smells must be measured because it will help the developers when determining the priority ...

Proposed framework to detect the severity of code smells depends on several machine learning models. LIME algorithm was further used to explain the machine learning model's predictions and interpretability. Prediction rules have been generated by PART ...

Social engineering attacks have surged with the increased reliance on online interactions. However, detecting these subtle deceptions remains challenging. This study proposes a novel machine learning approach to enhance social engineering attack ...

The problem of class imbalance has become a predominant area of research recently. Synthetic Minority Oversampling Technique (SMOTE) stands as a popular and widely adopted oversampling technique that effectively addresses the challenge of class ...

In this article, a complete methodology of a corpus realization of authentic Short Message Service (SMS) from Algerian dialect and which are transcribed in Latin characters or symbols is presented. A linguistic material constituted by 6,000 SMS coming ...

This research addresses the pitfalls and strategies for machine learning with small data sets in the context of sensor-based fatigue detection. It is shown that many existing studies in this area rely on small data sets and that classification results ...

SMOTE is an effective method for balancing imbalanced datasets by interpolating between existing samples in the minority class. However, if the synthetic samples generated through interpolation are based on noisy data points, then they may also be ...

Frequency‐time selective fading degrades the performance of communication systems, but it also provides an opportunity to collect multipath diversity and Doppler diversity. In this paper, an oversampled grouped‐linear‐constellation‐precoding (GLCP)...

Under doubly selective fading channels, the OFDM system can obtain more multipath‐Doppler diversity gains through oversampling and GLCP coding techniques. image image

Most of the flight accident data have uneven distribution of categories. When the traditional classifier is applied to this data, it will pay less attention to the minority class data. Synthetic Minority Over-sampling Technique (SMOTE), and its ...

Disease diagnosis is very important in the medical field. It is essential to diagnose chronic diseases such as diabetes, heart disease, cancer, and kidney diseases in the early stage. In recent times, ensembled-based approaches giving effective ...

Online services have advanced to the point where they have made our lives much easier, but many problems should be solved to make these services safer for consumers. Numerous transactions are conducted daily, and much personal information is published and ...

Imbalanced data classification has received much attention in machine learning, and many oversampling methods exist to solve this problem. However, these methods may suffer from insufficient noise filtering, overlap between synthetic and original samples,...

Synthetic Minority Oversampling Technique (SMOTE) and some extensions based on it are popularly used to balance imbalanced data. In this study, we concentrate on solving overfitting of the classification model caused by choosing instances to oversample ...

Multiple data streams from sensing devices in intelligent settings have improved life quality thanks to the internet of things (IoT). Anomalies and imbalanced data sources are unavoidable due to system complexity and IoT device rollout issues. An ...

Existing machine-learning research aims to improve the predictive capability of datasets using various feature selection and classification models. In the intrusion detection, data consists of normal data and a minimal number of attack data. This data ...

In image classification task, imbalanced dataset is a problem that often occurs. Batik pattern data also suffers this problem, mainly because of the poor quality of available images and rarity of certain patterns. In this research, we employed a ...

Class imbalance and incompleteness are the two most serious problems faced in data science and machine learning when working on real-life datasets. Both of these cases have severe implications on the ability of classification algorithms to make ...

Sentiment analysis is a technique of analyzing text to classify its emotion into positive, negative, or neutral sentiments. The main purpose of this study is to use sentiment analysis to seek Indonesian opinions about the chief of the Indonesian ...