کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947640 | 1439589 | 2017 | 27 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Dynamic extreme learning machine for data stream classification
ترجمه فارسی عنوان
پویایی دستگاه یادگیری افراطی برای طبقه بندی جریان داده
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کلمات کلیدی
جریان داده ها، طبقه بندی، مفهوم رانش دستگاه یادگیری شدید یادگیری آنلاین،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
In our society, many fields have produced a large number of data streams. How to mining the interesting knowledge and patterns from continuous data stream becomes a problem which we have to solve. Different from conventional classification algorithms, data stream classification algorithms have to adjust their classification models with the change of data stream because of concept drift. However, conventional classification models will keep stable once models are trained. To solve the problem, a dynamic extreme learning machine for data stream classification (DELM) is proposed. DELM utilizes online learning mechanism to train ELM as basic classifier and trains a double hidden layer structure to improve the performance of ELM. When an alert about concept drift is set, more hidden layer nodes are added into ELM to improve the generalization ability of classifier. If the value measuring concept drift reaches the upper limit or the accuracy of ELM is in a low level, the current classifier will be deleted, and the algorithm will use new data to train a new classifier so as to learn new concept. The experimental results showed DELM could improve the accuracy of classification result, and can adapt to new concept in a short time.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 238, 17 May 2017, Pages 433-449
Journal: Neurocomputing - Volume 238, 17 May 2017, Pages 433-449
نویسندگان
Shuliang Xu, Junhong Wang,