کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10360784 | 869911 | 2005 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Selected tree classifier combination based on both accuracy and error diversity
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
This paper proposes a method for combining multiple tree classifiers based on both classifier ensemble (bagging) and dynamic classifier selection schemes (DCS). The proposed method is composed of the following procedures: (1) building individual tree classifiers based on bootstrap samples; (2) calculating the distance between all possible two trees; (3) clustering the trees based on single linkage clustering; (4) selecting two clusters by local region in terms of accuracy and error diversity; and (5) voting the results of tree classifiers selected in the two clusters. Empirical evaluation using publicly available data sets confirms the superiority of our proposed approach over other classifier combining methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 38, Issue 2, February 2005, Pages 191-197
Journal: Pattern Recognition - Volume 38, Issue 2, February 2005, Pages 191-197
نویسندگان
H.W. Shin, S.Y. Sohn,