کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6856171 1437948 2018 27 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
چکیده انگلیسی
Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification algorithm. Such techniques, called oversamplers, modify the training data, allowing any classifier to be used with class-imbalanced datasets. Many algorithms have been proposed for this task, but most are complex and tend to generate unnecessary noise. This work presents a simple and effective oversampling method based on k-means clustering and SMOTE (synthetic minority oversampling technique), which avoids the generation of noise and effectively overcomes imbalances between and within classes. Empirical results of extensive experiments with 90 datasets show that training data oversampled with the proposed method improves classification results. Moreover, k-means SMOTE consistently outperforms other popular oversampling methods. An implementation1 is made available in the Python programming language.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 465, October 2018, Pages 1-20
نویسندگان
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