Consensus Clustering-Based Undersampling Approach to Imbalanced Learning
المؤلف
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-03-03
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Class imbalance is an important problem, encountered in machine learning applications, where one class (named as, the minority class) has extremely small number of instances and the other class (referred as, the majority class) has immense quantity of instances.
Imbalanced datasets can be of great importance in several real-world applications, including medical diagnosis, malware detection, anomaly identification, bankruptcy prediction, and spam filtering.
In this paper, we present a consensus clustering based-undersampling approach to imbalanced learning.
In this scheme, the number of instances in the majority class was undersampled by utilizing a consensus clustering-based scheme.
In the empirical analysis, 44 small-scale and 2 large-scale imbalanced classification benchmarks have been utilized.
In the consensus clustering schemes, five clustering algorithms (namely, k-means, k-modes, k-means++, self-organizing maps, and DIANA algorithm) and their combinations were taken into consideration.
In the classification phase, five supervised learning methods (namely, naïve Bayes, logistic regression, support vector machines, random forests, and k-nearest neighbor algorithm) and three ensemble learner methods (namely, AdaBoost, bagging, and random subspace algorithm) were utilized.
The empirical results indicate that the proposed heterogeneous consensus clustering-based undersampling scheme yields better predictive performance.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Onan, Aytuğ. 2019. Consensus Clustering-Based Undersampling Approach to Imbalanced Learning. Scientific Programming،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1210743
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Onan, Aytuğ. Consensus Clustering-Based Undersampling Approach to Imbalanced Learning. Scientific Programming No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1210743
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Onan, Aytuğ. Consensus Clustering-Based Undersampling Approach to Imbalanced Learning. Scientific Programming. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1210743
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1210743
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