کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
393319 665633 2014 27 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A new approach for imbalanced data classification based on data gravitation
ترجمه فارسی عنوان
یک رویکرد جدید برای طبقه بندی داده های عدم تعادل بر اساس گرانروی داده ها
کلمات کلیدی
گرانش داده، طبقه بندی، داده های نامتعادل، فراگیری ماشین
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Imbalanced classification is an important machine learning research topic that troubles most general classification models because of the imbalanced class distribution. A newly developed physical-inspired classification method, i.e., the data gravitation-based classification (DGC) model, performs well in many general classification problems. However, like other general classifiers, the performance of DGC suffers in imbalanced tasks. In this study, we develop a specific DGC model namely Imbalanced DGC (IDGC) model for imbalanced problems. The amplified gravitation coefficient (AGC) is introduced for gravitation computing. AGC is a type of coefficient that contains class imbalance information, which can strengthen and weaken the gravitational field of the minority and majority classes. We also design a fitness evaluation function in the weight optimization procedure of the data distribution to ensure that the model parameters adapt to the imbalanced class distributions. A total of 44 binary class data sets and 15 multiclass imbalanced data sets are used to test the performance of the proposed method. Experimental results show that the adapted DGC model is effective for imbalanced problems.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 288, 20 December 2014, Pages 347–373
نویسندگان
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