Binary biogeography-based optimization based SVM-RFE for feature selection

Albashish, Dheeb; Hammouri, Abdelaziz I.; Braik, Malik; Atwan, Jaffar; Sahran, Shahnorbanun

doi:10.1016/j.asoc.2020.107026

Cited by 110 publications

(41 citation statements)

References 66 publications

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“…Cross-validation (CV) (Albashish et al, 2021) is a resampling route to evaluate AI models on a limited-size dataset. Figure 10 shows the diagram of the K-fold CV.…”

Section: Cross-validationmentioning

confidence: 99%

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

Wang

Zhou

Yang

et al. 2021

Front. Aging Neurosci.

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Aim: Alzheimer's disease is a neurodegenerative disease that causes 60–70% of all cases of dementia. This study is to provide a novel method that can identify AD more accurately.Methods: We first propose a VGG-inspired network (VIN) as the backbone network and investigate the use of attention mechanisms. We proposed an Alzheimer's Disease VGG-Inspired Attention Network (ADVIAN), where we integrate convolutional block attention modules on a VIN backbone. Also, 18-way data augmentation is proposed to avoid overfitting. Ten runs of 10-fold cross-validation are carried out to report the unbiased performance.Results: The sensitivity and specificity reach 97.65 ± 1.36 and 97.86 ± 1.55, respectively. Its precision and accuracy are 97.87 ± 1.53 and 97.76 ± 1.13, respectively. The F1 score, MCC, and FMI are obtained as 97.75 ± 1.13, 95.53 ± 2.27, and 97.76 ± 1.13, respectively. The AUC is 0.9852.Conclusion: The proposed ADVIAN gives better results than 11 state-of-the-art methods. Besides, experimental results demonstrate the effectiveness of 18-way data augmentation.

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“…Cross-validation (CV) (Albashish et al, 2021) is a resampling route to evaluate AI models on a limited-size dataset. Figure 10 shows the diagram of the K-fold CV.…”

Section: Cross-validationmentioning

confidence: 99%

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

Wang

Zhou

Yang

et al. 2021

Front. Aging Neurosci.

View full text Add to dashboard Cite

show abstract

“…Basically, it prunes the least important features iteratively according to the weights derived from the classifier until the desired number of features to select is reached. It has also been widely used in feature selection and achieved good results ( Fernandez-Lozano et al, 2014 ; Xue et al, 2018 ; Albashish et al, 2021 ). The list of hyperparameters available for all dimensionality reduction methods is shown in Supplementary Table 2 .…”

Section: Methodsmentioning

confidence: 99%

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

et al. 2021

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Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.

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“…Therefore, to demonstrate the model's effectiveness in feature extraction, the extracted features were utilized for training the support vector machine (SVM) classifier and testing it with the same test set. The SVM classifier often shows the best results in many tasks, especially in binary classification (Alzubaidi et al, 2020c, Mohammadi et al, 2021Albashish et al, 2021). Therefore, we employed it in this paper.…”

Section: Feature Extraction In the Proposed Modelmentioning

confidence: 99%

Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study

Alzubaidi

Duan

Al-Dujaili

et al. 2021

PeerJ Computer Science

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Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pre-trained models have shown an effective performance in several domains of application, those models may not offer significant benefits in all instances when dealing with medical imaging scenarios. Such models were designed to classify a thousand classes of natural images. There are fundamental differences between these models and those dealing with medical imaging tasks regarding learned features. Most medical imaging applications range from two to ten different classes, where we suspect that it would not be necessary to employ deeper learning models. This paper investigates such a hypothesis and develops an experimental study to examine the corresponding conclusions about this issue. The lightweight convolutional neural network (CNN) model and the pre-trained models have been evaluated using three different medical imaging datasets. We have trained the lightweight CNN model and the pre-trained models with two scenarios which are with a small number of images once and a large number of images once again. Surprisingly, it has been found that the lightweight model trained from scratch achieved a more competitive performance when compared to the pre-trained model. More importantly, the lightweight CNN model can be successfully trained and tested using basic computational tools and provide high-quality results, specifically when using medical imaging datasets.

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Binary biogeography-based optimization based SVM-RFE for feature selection

Cited by 110 publications

References 66 publications

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study

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