Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles
المؤلفون المشاركون
Hu, Ruihan
Zhou, Songbin
Liu, Yisen
Tang, Zhiri
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-06-03
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
The ensemble pruning system is an effective machine learning framework that combines several learners as experts to classify a test set.
Generally, ensemble pruning systems aim to define a region of competence based on the validation set to select the most competent ensembles from the ensemble pool with respect to the test set.
However, the size of the ensemble pool is usually fixed, and the performance of an ensemble pool heavily depends on the definition of the region of competence.
In this paper, a dynamic pruning framework called margin-based Pareto ensemble pruning is proposed for ensemble pruning systems.
The framework explores the optimized ensemble pool size during the overproduction stage and finetunes the experts during the pruning stage.
The Pareto optimization algorithm is used to explore the size of the overproduction ensemble pool that can result in better performance.
Considering the information entropy of the learners in the indecision region, the marginal criterion for each learner in the ensemble pool is calculated using margin criterion pruning, which prunes the experts with respect to the test set.
The effectiveness of the proposed method for classification tasks is assessed using datasets.
The results show that margin-based Pareto ensemble pruning can achieve smaller ensemble sizes and better classification performance in most datasets when compared with state-of-the-art models.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hu, Ruihan& Zhou, Songbin& Liu, Yisen& Tang, Zhiri. 2019. Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129576
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hu, Ruihan…[et al.]. Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1129576
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hu, Ruihan& Zhou, Songbin& Liu, Yisen& Tang, Zhiri. Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129576
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129576
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر