A Review on Imbalanced Data Classification Techniques

Basha, Shaik Johny; Madala, Srinivasa Rao; Vivek, Kolla; Kumar, Eedupalli Sai; Ammannamma, Tamminina

doi:10.1109/icacta54488.2022.9753392

Cited by 31 publications

(2 citation statements)

References 21 publications

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“…The most important thing while doing this is that all classes have same attention and weights, so, even if a class has few samples, we gave it a good weight and the classifier was able to recognize this class more effectively than before. The final performance of the overall model without cost-sensitive or any other technique that deal with imbalance data, could be very high, but when we give in details, we found that the model lack to recognize effectively or with high accuracy some classes (mainly those with less data) [28], [29].…”

Section: Resultsmentioning

confidence: 99%

Improve malware classifiers performance using cost-sensitive learning for imbalanced dataset

Ouahab

Elaachak

Bouhorma

2023

IJ-AI

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In recent times, malware visualization has become very popular for malware classification in cybersecurity. Existing malware features can easily identify known malware that have been already detected, but they cannot identify new and infrequent malwares accurately. Moreover, deep learning algorithms show their power in term of malware classification topic. However, we found the use of imbalanced data; the Malimg database which contains 25 malware families don’t have same or near number of images per class. To address these issues, this paper proposes an effective malware classifier, based on costsensitive deep learning. When performing classification on imbalanced data, some classes get less accuracy than others. Cost-sensitive is meant to solve this issue, however in our case of 25 classes, classical cost-sensitive weights wasn’t effective is giving equal attention to all classes. The proposed approach improves the performance of malware classification, and we demonstrate this improvement using two Convolutional Neural Network models using functional and subclassing programming techniques, based on loss, accuracy, recall and precision.

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Section: Resultsmentioning

confidence: 99%

Improve malware classifiers performance using cost-sensitive learning for imbalanced dataset

Ouahab

Elaachak

Bouhorma

2023

IJ-AI

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“…These additional samples provide crucial information to the minority class, helping prevent misclassification of its instances. By employing oversampling techniques, the dataset becomes more balanced, leading to improved performance in classification tasks [25], [26], [27].…”

Section: ) Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

Putra,

Prasetyo Kusumo,

Prayitno

et al. 2024

JOIV : Int. J. Inform. Visualization

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Diabetes Mellitus (DM) as a non-communicable disease (NCD) continues to increase every year. Continuous glucose monitoring (CGM) is essential for effective DM management. However, existing disposable glucose monitoring methods still rely on invasive techniques, cause pain, and lack continuous monitoring capabilities. On the other hand, non-invasive techniques are not feasible for CGM due to the biometric data's complexity and the classification system's inadequate performance. This study aims to develop a non-invasive technology to improve the performance of a non-invasive blood glucose classification system using Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) and an oversampling technique. The oversampling technique could improve data quantity by balancing the amount of data for each class. This study recruited twenty-three participants in the age range of 20 to 22 years comprising seven females and fifteen males. During data recording sessions, blood glucose levels were simultaneously assessed using a gold-standard glucometer and a non-invasive CGM prototype. The proposed CNN model successfully improved the classification accuracy of non-invasive blood glucose monitoring significantly. With the implementation of oversampling for augmenting the data, the accuracy of the proposed model increased to more than 88%. This study concludes that non-invasive approaches combined with AI technology have the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly improving diabetes management and enhancing the overall quality of life for those affected by this condition. These findings could revolutionize the field of diabetes management, offering a more comfortable and accurate monitoring solution that could potentially transform the lives of millions of diabetes patients.

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A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support

Lee,

Yuan,

Man

et al. 2023

Communications in Computer and Information Science

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A Review on Imbalanced Data Classification Techniques

Cited by 31 publications

References 21 publications

Improve malware classifiers performance using cost-sensitive learning for imbalanced dataset

Improve malware classifiers performance using cost-sensitive learning for imbalanced dataset

Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support

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