Imbalanced Data Classification using Sampling Techniques and XGBoost
نویسندگان
چکیده
منابع مشابه
Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques
of the Thesis Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques
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با توجه به گسترش روز افزون تقلب در حوزه بیمه به خصوص در بخش بیمه اتومبیل و تبعات منفی آن برای شرکت های بیمه، به کارگیری روش های مناسب و کارآمد به منظور شناسایی و کشف تقلب در این حوزه امری ضروری است. درک الگوی موجود در داده های مربوط به مطالبات گزارش شده گذشته می تواند در کشف واقعی یا غیرواقعی بودن ادعای خسارت، مفید باشد. یکی از متداول ترین و پرکاربردترین راه های کشف الگوی داده ها استفاده از ر...
Borderline over-sampling for imbalanced data classification
Traditional classification algorithms, in many times, perform poorly on imbalanced data sets in which some classes are heavily outnumbered by the remaining classes. For this kind of data, minority class instances, which are usually much more of interest, are often misclassified. The paper proposes a method to deal with them by changing class distribution through oversampling at the borderline b...
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In many real world applications, the example data among different pattern classes are imbalanced and overlapping, which hinder the classification performance of many learning algorithms. In this paper, data cleaning techniques based BNF (the borderline noise factor) is proposed to remove the borderline noise and three under-sampling methods are studied to select the representative majority clas...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2018
ISSN: 0975-8887
DOI: 10.5120/ijca2018917735