کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6940245 | 1450009 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Redefining nearest neighbor classification in high-dimensional settings
ترجمه فارسی عنوان
تعریف مجدد نزدیکترین درجه همسایه در تنظیمات با ابعاد بزرگ
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کلمات کلیدی
نزدیکترین طبقه همسایه، مجموعه داده های با ابعاد بزرگ، معیارهای فاصله،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
In this work, a novel nearest neighbor approach is presented. The main idea is to redefine the distance metric in order to include only a subset of relevant variables, assuming that they are of equal importance for the classification model. Three different distance measures are redefined: the traditional squared Euclidean, the Manhattan, and the Chebyshev. These modifications are designed to improve classification performance in high-dimensional applications, in which the concept of distance becomes blurry, i.e., all training points become uniformly distant from each other. Additionally, the inclusion of noisy variables leads to a loss of predictive performance if the main patterns are contained in just a few variables, since they are equally weighted. Experimental results on low- and high-dimensional datasets demonstrate the importance of these modifications, leading to superior average performance in terms of Area Under the Curve (AUC) compared with the traditional k nearest neighbor approach.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 110, 15 July 2018, Pages 36-43
Journal: Pattern Recognition Letters - Volume 110, 15 July 2018, Pages 36-43
نویسندگان
Julio López, Sebastián Maldonado,