A new method for ensemble combination based on adaptive decision making

Hassan, Mohammed Falih; Abdel‐Qader, Ikhlas; Bazuin, Bradley J.

doi:10.1016/j.knosys.2021.107544

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…EL-APMC is a classification method proposed in 11 , which belongs to the family of ensemble learning methods. In this work, a theoretical framework is developed to understand how the fusion methods for ensemble learning systems interact with base classifiers.…”

Section: Proposed Methodsmentioning

confidence: 99%

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An effective ensemble learning approach for classification of glioma grades based on novel MRI features

Hassan,

Al-Zurfi,

Abed

et al. 2024

Sci Rep

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The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors’ prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

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Section: Proposed Methodsmentioning

confidence: 99%

“…To address these challenges, we proposed an automated classification model based on a novel feature extraction method integrated with an effective machine-learning algorithm called EL-APMC 11 . EL-APMC is built on an ensemble of base classifiers that adaptively combine to maximize classification results.…”

Section: Introductionmentioning

confidence: 99%

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

Hassan,

Al-Zurfi,

Abed

et al. 2024

Sci Rep

Self Cite

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“…To address this problem, some literature focused on increasing the accuracy of the classification algorithm on a limited-size dataset while others investigated the effect of the dataset size on the performance of the classification algorithm [24]. In this work, a novel feature extraction method and a classification algorithm called Ensemble Learning based on Adaptive Power Combiner (EL-APMC) is investigated [25]. In this work, EL-APMC as well as the state-of-the-art machine learning methods are investigated to tackle the problem of classification limited data size.…”

Section: Impact Of Dataset Size On Classification Performancementioning

confidence: 99%

An Effective Ensemble Learning Approach for Classification of Glioma Grades Based on Novel MRI Features

Hassan,

Al-Zurfi,

Abed

2023

Preprint

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The preoperative diagnosis of brain tumor grades is important for therapeutic planning as it contributes to the tumors' prognosis. In the last few years, the development in the field of artificial intelligence and machine learning contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. In addition, one of the biggest challenges faced by a physician in the medical field is how to assess the accurate grade of glioma that is due to the complexity of tumor descriptors that appear on medical images. In this work, we applied a new ensemble learning method based on novel MRI features to build a new classification system for glioma grading. Specifically, we assess the discriminatory ability of the selected features, integrated with the new ensemble learning method called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). Then we evaluate and compare the performance of the EL-APMC algorithm with 21 classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy and F1-score metrics over the MRI Brain Tumor dataset called BRAT2015. In addition, we showed that the differences in classification results among 22 classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

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Ensemble-based Deep Learning Approach for Performance Improvement of BIM Element Classification

Kim

Lee

et al. 2023

KSCE Journal of Civil Engineering

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A new method for ensemble combination based on adaptive decision making

Cited by 6 publications

References 30 publications

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

An Effective Ensemble Learning Approach for Classification of Glioma Grades Based on Novel MRI Features

Ensemble-based Deep Learning Approach for Performance Improvement of BIM Element Classification

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