کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4948373 | 1439611 | 2016 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Classification problems with an imbalanced class distribution have received an increased amount of attention within the machine learning community over the last decade. They are encountered in a growing number of real-world situations and pose a challenge to standard machine learning techniques. We propose a new hybrid method specifically tailored to handle class imbalance, called EPRENNID. It performs an evolutionary prototype reduction focused on providing diverse solutions to prevent the method from overfitting the training set. It also allows us to explicitly reduce the underrepresented class, which the most common preprocessing solutions handling class imbalance usually protect. As part of the experimental study, we show that the proposed prototype reduction method outperforms state-of-the-art preprocessing techniques. The preprocessing step yields multiple prototype sets that are later used in an ensemble, performing a weighted voting scheme with the nearest neighbor classifier. EPRENNID is experimentally shown to significantly outperform previous proposals.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 216, 5 December 2016, Pages 596-610
Journal: Neurocomputing - Volume 216, 5 December 2016, Pages 596-610
نویسندگان
Sarah Vluymans, Isaac Triguero, Chris Cornelis, Yvan Saeys,