کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7562332 | 1491507 | 2018 | 28 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Steel surface defect classification using multiple hyper-spheres support vector machine with additional information
ترجمه فارسی عنوان
طبقه بندی نقص سطح فولاد با استفاده از حوزه های چندگانه، دستگاه بردار با اطلاعات اضافی را پشتیبانی می کند
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کلمات کلیدی
نقص سطح فولاد، طبقه بندی چند طبقه، ماشین بردار پشتیبانی، حوزه های چندگانه، اطلاعات اضافی،
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی
A novel multiple hyper-spheres support vector machine with additional information (MHSVM+) is proposed for multi-class steel surface defects classification. Originated from binary twin hyper-spheres support vector machine, MHSVM+Â uses hyper-sphere to solve classification decision problem. Differently, MHSVM+ is a multi-class classifier, where it builds a corresponding hyper-sphere for each type of defect dataset. Moreover, MHSVM+Â introduces learning paradigm using additional information, which means it can learn additional information hidden in defect dataset. Two types of additional information are provided: local neighbor information and local density information. Local neighbor information contains local classification results for defect samples. And local density information is used to capture label noise, isolated samples and important samples in defect dataset. The above two types of additional information are introduced into MHSVM+Â model. Finally, MHSVM+Â classifier is used to classify six types of steel surface defects. Experimental results show that the novel multi-class classifier has perfect classification accuracy for defect dataset, especially corrupted defect dataset.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 172, 15 January 2018, Pages 109-117
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 172, 15 January 2018, Pages 109-117
نویسندگان
Rongfen Gong, Chengdong Wu, Maoxiang Chu,