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الرئيسية > A Novel Rotation Forest Modali...>الوصف

A Novel Rotation Forest Modality Based On Hybrid NNs: RF (ScPSO-NN)

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Ceylan, Rahime (Author)
مؤلفين آخرين: Koyuncu, Hasan (Co-Author)
المجلد/العدد: مج31, ع2
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2019
الصفحات: 235 - 251
DOI: 10.33948/0584-031-002-009
ISSN: 1319-1578
رقم MD: 974627
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
علوم الحاسوب | الخوارزميات | قواعد البيانات
كلمات المؤلف المفتاحية:
Rotation Forest | Particle Swarm Optimization | Artificial Bee Colony Optimization | Scout Particle Swarm Optimization | Hybrid Classifiers
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المستخلص: Neural Network (NN), hybrid NN methods and Rotation Forest (RF) ensemble classifier are preferred in pattern analysis owing to their ability for finding efficient solutions on different problems. NN architecture usually includes backpropagation type algorithms in which error is exposed to fluctuations. Hybrid NN methods are generally designed to improve the classification performance of NN. Scout Particle Swarm Optimization (ScPSO) is one of these optimization algorithms including the effective parts of Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC). Moreover, RF algorithm usually indicates the same performance as in hybrid NN methods, although it is comprised of Decision Tree (DT) classifiers. At this point, our paper investigates whether RF using the hybrid NNs can outperform other ensemble classifiers in binary-medical pattern classification, or not. With this intention, PSO, ABC and ScPSO are placed in NN algorithms instead of back propagation, and hybrid methods (PSO-NN, ABC-NN and ScPSO-NN) are realized. As a result, RF (PSO-NN), RF (ABC-NN) and RF (ScPSONN) architectures are obtained. Classification Accuracy (CA), Area Under Curve (AUC), Sensitivity, Specificity, F-measure, Gmean and Precision metrics are used for a statistical performance comparison, and a test based on 2-fold cross validation method was realized on five medical datasets. © 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
ISSN: 1319-1578

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