کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9743581 | 1491194 | 2005 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A generalized boosting algorithm and its application to two-class chemical classification problem
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
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چکیده انگلیسی
Boosting is one of the most important recent developments in classification methodology. It can significantly improve the prediction performance of any single classification algorithm and has been successfully applied to many different fields including problems in chemometrics. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data, and then taking a weighted majority vote of the sequence of classifiers thus produced. In this paper, we proposed a generalized boosting algorithm via Bayes optimal decision rule. Using Bayes optimal decision rule, we adjust the weights of the sequence of classifiers in the voting process of boosting algorithm. The two types of errors are introduced into the generalized boosting and make the voting process more sensible. Meanwhile, the weights of the training samples are also correspondingly adjusted according to some criterion. The generalized boosting is applied to the binary classification for chemical data. Experimental results show that it can improve the predict accuracy compared with AdaBoost algorithm especially when the difference between the two types of errors for classification is large.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Analytica Chimica Acta - Volume 543, Issues 1â2, 6 July 2005, Pages 181-191
Journal: Analytica Chimica Acta - Volume 543, Issues 1â2, 6 July 2005, Pages 181-191
نویسندگان
Ping He, Kai-Tai Fang, Yi-Zeng Liang, Bo-Yan Li,