Handling Class Imbalance Problem in Binary Classification

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Natural processes often generate some observations more frequently than others. These processes result in an unbalanced distributions which cause the classifiers to bias toward the majority class especially because most classifiers assume a normal distribution. The quantity and the diversity of imbalanced application domains necessitate and motivate the research community to address the topic of imbalanced dataset classification. Therefore, imbalanced datasets are attracting an incremental attention in the field of classification. In this work, we address the necessity of adapting data pre-processing models in the framework of binary imbalanced datasets, focusing on the synergy with the different cost-sensitive and class imbalance classification algorithms. The results of this empirical study favored the Synthetic Minority Over-sampling Technique (SMOTE) in the case of relativity high Imbalance Ratio (IR) and favored Neighborhood Cleaning Rule (NCL) in the case of relativity small IR. Further improvement was suggested to enhance NCL scalability with IR, and the proposed method is named NCL+. The outcomes showed that NCL+ outperformed NCL especially with the datasets of relatively high IR.

روابط وملفات

التعريف والنوع

رقم الوثيقة

fed65a5b-7bff-4eb9-87f6-e80d6028c5f7

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2283854_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" U Zeyar Aung ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Nadyah Alabdouli","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

Handling Class Imbalance Problem in Binary Classification

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Data processing; Binary Classification; Imbalanced Data

إشراف وإعداد

الإشراف

U Zeyar Aung

الإعداد

Nadyah Alabdouli

الاقتباسات الببليوغرافية