کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6856394 | 1437955 | 2018 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Query-by-committee improvement with diversity and density in batch active learning
ترجمه فارسی عنوان
بهبود پرس و جو توسط کمیته با تنوع و چگالی در یادگیری فعال دسته ای
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کلمات کلیدی
یادگیری فعال دسته ای، تراکم، تنوع درخواست توسط کمیته، جنگل تصادفی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Active learning has gained attention as a method to expedite the learning curve of classifiers. To this end, uncertainty sampling is a widely adopted strategy that selects instances closer to the decision boundary. However, uncertainty sampling alone may not be sufficient in batch active learning due to the redundancy of instances and its susceptibility to outliers. In this study, we utilize query-by-committee (QBC) for uncertainty and demonstrate that its performance can be improved by introducing diversity and density in instance utility. Test results show that uncertainty sampling by QBC can be significantly improved with diversity and density incorporated in instance selection. Furthermore, we investigate several distance measures for use in diversity and density and show that random forest dissimilarity can be an effective distance measure in batch active learning. The effects of the characteristics of the data on the results are also analyzed.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volumes 454â455, July 2018, Pages 401-418
Journal: Information Sciences - Volumes 454â455, July 2018, Pages 401-418
نویسندگان
Seho Kee, Enrique del Castillo, George Runger,