کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4963275 | 1447004 | 2017 | 33 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Cost-Sensitive back-propagation neural networks with binarization techniques in addressing multi-class problems and non-competent classifiers
ترجمه فارسی عنوان
شبکه های عصبی با پشت سر گذاشتن هزینه های حساس با تکنیک های باینری در رفع مشکلات چند طبقه و طبقه بندی های نامناسب
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کلمات کلیدی
یادگیری حساس شبکه های عصبی، یک به یک، استراتژیهای جمعآوری، انتخاب طبقه بندی پویا،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Multi-class classification problems can be addressed by using decomposition strategy. One of the most popular decomposition techniques is the One-vs-One (OVO) strategy, which consists of dividing multi-class classification problems into as many as possible pairs of easier-to-solve binary sub-problems. To discuss the presence of classes with different cost, in this paper, we examine the behavior of an ensemble of Cost-Sensitive Back-Propagation Neural Networks (CSBPNN) with OVO binarization techniques for multi-class problems. To implement this, the original multi-class cost-sensitive problem is decomposed into as many sub-problems as possible pairs of classes and each sub-problem is learnt in an independent manner using CSBPNN. Then a combination method is used to aggregate the binary cost-sensitive classifiers. To verify the synergy of the binarization technique and CSBPNN for multi-class cost-sensitive problems, we carry out a thorough experimental study. Specifically, we first develop the study to check the effectiveness of the OVO strategy for multi-class cost-sensitive learning problems. Then, we develop a comparison of several well-known aggregation strategies in our scenario. Finally, we explore whether further improvement can be achieved by using the management of non-competent classifiers. The experimental study is performed with three types of cost matrices and proper statistical analysis is employed to extract the meaningful findings.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 56, July 2017, Pages 357-367
Journal: Applied Soft Computing - Volume 56, July 2017, Pages 357-367
نویسندگان
Zhong-Liang Zhang, Xing-Gang Luo, Salvador GarcÃa, Francisco Herrera,