A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM
المؤلفون المشاركون
Feng, Yanghe
Wang, Qi
Luo, ZhiHao
Huang, JinCai
Liu, Zhong
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
2017-01-30
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains.
Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model.
Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones.
However, the samples near the decision boundary which contain more discriminative information should be valued and the skew of the boundary would be corrected by constructing synthetic samples.
Inspired by the truth and sense of geometry, we designed a new synthetic minority oversampling technique to incorporate the borderline information.
What is more, ensemble model always tends to capture more complicated and robust decision boundary in practice.
Taking these factors into considerations, a novel ensemble method, called Bagging of Extrapolation Borderline-SMOTE SVM (BEBS), has been proposed in dealing with imbalanced data learning (IDL) problems.
Experiments on open access datasets showed significant superior performance using our model and a persuasive and intuitive explanation behind the method was illustrated.
As far as we know, this is the first model combining ensemble of SVMs with borderline information for solving such condition.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Qi& Luo, ZhiHao& Huang, JinCai& Feng, Yanghe& Liu, Zhong. 2017. A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1139843
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Qi…[et al.]. A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1139843
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Qi& Luo, ZhiHao& Huang, JinCai& Feng, Yanghe& Liu, Zhong. A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1139843
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139843
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