Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines
المؤلفون المشاركون
Zhang, Dezheng
Luo, Xiong
Shaheryar, Ahmad
Ali, Hazrat
Abuassba, Adnan O. M.
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-05-04
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN).
It often has good generalization performance.
However, there are chances that it might overfit the training data due to having more hidden nodes than needed.
To address the generalization performance, we use a heterogeneous ensemble approach.
We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM.
The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling.
The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble.
Finally, the class label of unseen data is predicted using majority vote approach.
Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble).
The validity of AELME is confirmed through classification on several real-world benchmark datasets.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Abuassba, Adnan O. M.& Zhang, Dezheng& Luo, Xiong& Shaheryar, Ahmad& Ali, Hazrat. 2017. Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1140898
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Abuassba, Adnan O. M.…[et al.]. Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1140898
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Abuassba, Adnan O. M.& Zhang, Dezheng& Luo, Xiong& Shaheryar, Ahmad& Ali, Hazrat. Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1140898
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1140898
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