Adjusting the imbalance ratio by the dimensionality of imbalanced data

Zhu, Rui; Guo, Yiwen; Xue, Jing-Hao

doi:10.1016/j.patrec.2020.03.004

Cited by 57 publications

(21 citation statements)

References 10 publications

(12 reference statements)

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“…Here, the ratio of the majority sample number to the minority sample is 6.5. By the SMOTE technique, this ratio was reduced to 1 [9] , [10] . The SMOTE method is based on the algorithm which is given below [32] .…”

Section: Methodsmentioning

confidence: 99%

“…The most widely used class imbalance measure in the literature is calculated as the ratio of the sample numbers of the largest majority class and the smallest minority class and is called the imbalance ratio. The higher this ratio is the greater the imbalance scope of the dataset and causes over fitting problem in the classification process and decreases performance [9] , [10] . A widely used method to eliminate the imbalance between data classes encountered by Deep Learning classifier models is SMOTE (Synthetic Minority Over-sampling Technique) method.…”

Section: Introductionmentioning

confidence: 99%

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A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings

Göreke

Sarı

Koçkanat

2021

Applied Soft Computing

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Unfortunately, Coronavirus disease 2019 (COVID-19) is spreading rapidly all over the world. Along with causing many deaths, it has substantially affected the social life, economics, and infrastructure worldwide in a negative manner. Therefore, it is very important to be able to diagnose the COVID-19 quickly and correctly. In this study, a new feature group based on laboratory findings was obtained considering ethnical and genetic differences for interpretation of blood data. Then, using this feature group, a new hybrid classifier architecture based on deep learning was designed and COVID-19 detection was made. Classification performance indicators were obtained as accuracy of 94.95%, F1-score of 94.98%, precision of 94.98%, recall of 94.98% and AUC of 100%. Achieved results were compared with those of the deep learning classifiers suggested in literature. According to these results, proposed method shows superior performance and can provide more convenience and precision to experts for diagnosis of COVID-19 disease.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings

Göreke

Sarı

Koçkanat

2021

Applied Soft Computing

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“…The nearly equal numbers of five different arrhythmias which are N, LBBB, RBBB, PVC, and APB are selected to balance the dataset and classified to achieve the highest accuracy. Recent studies have revealed that data augmentation techniques and providing an equal number of beat samples among classes of the dataset can be used as approaches to stabilize the imbalance ratio (IR) [37].…”

Section: Ecg Databasementioning

confidence: 99%

Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks

Degirmenci

Özdemir

Izci

et al. 2022

IRBM

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Background: Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG recordings is not practical for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to develop computer-aided-diagnosis (CAD) systems to automatically identify arrhythmias. Methods: This paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The proposed approach identifies arrhythmia classes using Convolutional Neural Network (CNN) trained by two-dimensional (2D) ECG beat images. Firstly, ECG signals, which consist of 5 different arrhythmias, are segmented into heartbeats which are transformed into 2D grayscale images. Afterward, the images are used as input for training a new CNN architecture to classify heartbeats. Results:The experimental results show that the classification performance of the proposed approach reaches an overall accuracy of 99.7%, sensitivity of 99.7%, and specificity of 99.22% in the classification of five different ECG arrhythmias.Further, the proposed CNN architecture is compared to other popular CNN architectures such as LeNet and ResNet-50 to evaluate the performance of the study. Conclusions:Test results demonstrate that the deep network trained by ECG images provides outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Moreover, the proposed method has lower computational costs compared to existing methods and is more suitable for mobile device-based diagnosis systems as it does not involve any complex preprocessing process. Hence, the proposed approach provides a simple and robust automatic cardiac arrhythmia detection scheme for the classification of ECG arrhythmias.

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“…The imbalance ratio [18] is defined as R= Smaj/Smin where Smaj is the number of majority class samples, and Smin is the number of samples of the minority class [19]. The greater the value of R, the higher is the imbalance.…”

Section: Processing Skewed Datasetmentioning

confidence: 99%

A Novel Ensemble Framework for an Intelligent Intrusion Detection System

Seth¹,

Chahal

Singh

2021

IEEE Access

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Background: Building an effective Intrusion detection system in a multi-attack classification environment is challenging due to the diversity of modern, sophisticated attacks. High-performance classification methods are needed for Intrusion Detection Systems as attackers can establish intrusive methods and easily evade the detection tools deployed in a computing environment. Moreover, it is challenging to use a single classifier to efficiently detect all kinds of attacks. Aims: To propose a unique ensemble framework that can effectively detect different attack categories. Method: The proposed approach is based on building an ensemble by ranking the detection ability of different base classifiers to identify various types of attacks. The F1-score of an algorithm is used to compute the rank matrix for different attack categories. For final prediction algorithm's output for an attack is only considered if the algorithm has the highest F1-Score in the rank matrix for the particular attack category. This approach contrasts with the voting approach where the final classification is based on the voting of all classifiers in the ensemble irrespective of the fact if the algorithm is efficient enough to detect that attack or not. Results: With the proposed method, the final accuracy obtained is 96.97 %, a recall rate of 97.4%, and a better attack detection rate than the baseline classifiers and other existing approaches for different attack categories.

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Adjusting the imbalance ratio by the dimensionality of imbalanced data

Cited by 57 publications

References 10 publications

A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings

A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings

Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks

A Novel Ensemble Framework for an Intelligent Intrusion Detection System

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