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Your search keyword '"Benzebouchi, Nacer"' showing total 3 results
Start Over Author "Benzebouchi, Nacer" Topic ensemble learning
3 results on '"Benzebouchi, Nacer"'

1. SMOTE–ENN-Based Data Sampling and Improved Dynamic Ensemble Selection for Imbalanced Medical Data Classification

Author

Lamari, Mouna, Azizi, Nabiha, Hammami, Nacer Eddine, Boukhamla, Assia, Cheriguene, Soraya, Dendani, Najdette, Benzebouchi, Nacer Eddine, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Saeed, Faisal, editor, Al-Hadhrami, Tawfik, editor, Mohammed, Fathey, editor, and Mohammed, Errais, editor

Published
2021
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2. Unbalanced Learning for Early Automatic Diagnosis of Diabetes Based on Enhanced Resampling Technique and Stacking Classifier

Author

Zemmal, Nawel, Benzebouchi, Nacer, Azizi, Nabiha, Schwab, Didier, Belhaouari, Brahim, and Schwab, Didier

Subjects
Unbalanced learning, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], meta-learning, diabetes diagnosis, multimodal classification, ensemble learning
Abstract

Diabetes is characterized by an abnormally enhanced concentration of glucose in the blood serum. It has a damaging impact on several noble body systems, mainly on the cardiovascular, renal, and visual systems. Automated screening allows early diagnosis of certain illness (such as diabetes), which generally increases the chances for successful treatment. Today, machine learning has developed considerably in the domain of medical diagnosis, especially with regard to diabetes diagnosis, and as such, thanks to the integration of the concept of unbalanced learning, which considerably reduces the generation of erroneous classification results. This general concept is dealt with from two different perspectives, i.e. at the data level through modification/balancing of the learning data set as well as at the algorithm level. The present paper takes a hybrid approach towards imbalanced learning in proposing an enhanced multimodal meta-learning method called IRESAMPLE+St to distinguish between normal and diabetic patients. This approach relies on the Stacking paradigm by utilizing the complementarity that may exist between classifiers. In the same focus of this study, a modified RESAMPLE-based technique referred to as IRESAMPLE+ and the SMOTE method is integrated as a preliminary resampling step to overcome and resolve the problem of unbalanced data. The imbalanced Pima Indian Diabetes (PID) data set is optimized through the proposed IRESAMPLE+ method, successfully operating as both an oversampling and undersampling technique, thereby reinforcing the diagnostic accuracy established by the Stacking classifier. The suggested IRESAMPLE+St provides a computerized diabetes diagnostic system with impressive results, Accuracy of 99.87%, Sensitivity of 100%, Specificity of 99.70% and AUROC of 99.90%, comparing them to the principal related studies. The over-performing results reflect the design and engineering successes achieved with the IRESAMPLE+St system for the classification of diabetes.

Published
2022

3. Multi-modal classifier fusion with feature cooperation for glaucoma diagnosis.

Author

Benzebouchi, Nacer Eddine, Azizi, Nabiha, Ashour, Amira S., Dey, Nilanjan, and Sherratt, R. Simon

Subjects
*ARTIFICIAL neural networks, *GLAUCOMA, *SUPPORT vector machines, *OPTIC nerve, *LINEAR complementarity problem, *NOSOLOGY, *HILBERT-Huang transform
Abstract

Glaucoma is a major public health problem that can lead to an optic nerve lesion, requiring systematic screening in the population over 45 years of age. The diagnosis and classification of this disease have had a marked and excellent development in recent years, particularly in the machine learning domain. Multimodal data have been shown to be a significant aid to the machine learning domain, especially by its contribution to improving data driven decision-making. Solving classification problems by combinations of classifiers has made it possible to increase the robustness as well as the classification reliability by using the complementarity that may exist between the classifiers. Complementarity is considered a key property of multimodality. A Convolutional Neural Network (CNN) works very well in pattern recognition and has been shown to exhibit superior performance, especially for image classification which can learn by themselves useful features from raw data. This article proposes a multimodal classification approach based on deep Convolutional Neural Network and Support Vector Machine (SVM) classifiers using multimodal data and multimodal feature for glaucoma diagnosis from retinal fundus images from RIM-ONE dataset. We make use of handcrafted feature descriptors such as the Gray Level Co-Occurrence Matrix, Central Moments and Hu Moments to co-operate with features automatically generated by the CNN in order to properly detect the optic nerve and consequently obtain a better classification rate, allowing a more reliable diagnosis of glaucoma. The experimental results confirm that the combination of classifiers using a new hybrid fusion approach and the BWWV technique is better than learning classifiers separately. The proposed method provides a computerized diagnosis system for glaucoma disease with impressive results comparing them to the main related studies that allow us to continue in this research path. [ABSTRACT FROM AUTHOR]

Published
2019
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