Ensemble of Rotation Trees for Imbalanced Medical Datasets
المؤلفون المشاركون
Guo, Huaping
Liu, Haiyan
Liu, Wei
She, Wei
Wu, Chang-an
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-04-10
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Medical datasets are often predominately composed of “normal” examples with only a small percentage of “abnormal” ones and how to correctly recognize the abnormal examples is very meaningful.
However, conventional classification learning methods try to pursue high accuracy by assuming that the number of any class examples is similar to each other, which lead to the fact that the abnormal class examples are usually ignored and misclassified to normal ones.
In this paper, we propose a simple but effective ensemble method called ensemble of rotation trees (ERT) to handle this problem in imbalanced medical datasets.
ERT learns an ensemble through the following four stages: (1) undersampling subsets from normal class, (2) obtaining new balanced training sets through combining each subset and abnormal class, (3) inducing a rotation matrix on randomly sampling subset of each new balanced set, and in each rotation matrix space, (4) learning a decision tree on each balanced training data.
Here, the rotation matrix is mainly to improve the diversity between ensemble members, and undersampling technique aims to improve the performance of learned models on abnormal class.
Experimental results show that, compared with other state-of-the-art methods, ERT shows significantly better performance for imbalanced medical datasets.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Guo, Huaping& Liu, Haiyan& Wu, Chang-an& Liu, Wei& She, Wei. 2018. Ensemble of Rotation Trees for Imbalanced Medical Datasets. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1191308
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Guo, Huaping…[et al.]. Ensemble of Rotation Trees for Imbalanced Medical Datasets. Journal of Healthcare Engineering No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1191308
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Guo, Huaping& Liu, Haiyan& Wu, Chang-an& Liu, Wei& She, Wei. Ensemble of Rotation Trees for Imbalanced Medical Datasets. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1191308
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1191308
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