کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
13430337 | 1842417 | 2020 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fused variable screening for massive imbalanced data
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Imbalanced data, in which the data exhibit an unequal or highly-skewed distribution between its classes/categories, are pervasive in many scientific fields, with application range from bioinformatics, text classification, face recognition, fraud detection, etc. Imbalanced data in modern science are often of massive size and high dimensionality, for example, gene expression data for diagnosing rare diseases. To address this issue, a fused screening procedure is proposed for dimension reduction with large-scale high dimensional imbalanced data under repeated case-control samplings. There are several advantages of the proposed method: it is model-free without any model specification for the underlying distribution; it is relatively inexpensive in computational cost by using the subsampling technique; it is robust to outliers in the predictors. The theoretical properties are established under regularity conditions. Numerical studies including extensive simulations and a real data example confirm that the proposed method performs well in practical settings.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 141, January 2020, Pages 94-108
Journal: Computational Statistics & Data Analysis - Volume 141, January 2020, Pages 94-108
نویسندگان
Jinhan Xie, Meiling Hao, Wenxin Liu, Yuanyuan Lin,