کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
408435 | 679028 | 2011 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Statistical approaches to combining binary classifiers for multi-class classification
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
One of the popular methods for multi-class classification is to combine binary classifiers. In this paper, we propose a new approach for combining binary classifiers. Our method trains a combining method of binary classifiers using statistical techniques such as penalized logistic regression, stacking, and a sparsity promoting penalty. Our approach has several advantages. Firstly, our method outperforms existing methods even if the base classifiers are well-tuned. Secondly, an estimate of conditional probability for each class can be naturally obtained. Furthermore, we propose selecting relevant binary classifiers by adding the group lasso type penalty in training the combining method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 74, Issue 5, February 2011, Pages 680–688
Journal: Neurocomputing - Volume 74, Issue 5, February 2011, Pages 680–688
نویسندگان
Yuichi Shiraishi, Kenji Fukumizu,