کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10360797 | 869916 | 2005 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Bayesian network classifiers versus selective k-NN classifier
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
In this paper Bayesian network classifiers are compared to the k-nearest neighbor (k-NN) classifier, which is based on a subset of features. This subset is established by means of sequential feature selection methods. Experimental results on classifying data of a surface inspection task and data sets from the UCI repository show that Bayesian network classifiers are competitive with selective k-NN classifiers concerning classification accuracy. The k-NN classifier performs well in the case where the number of samples for learning the parameters of the Bayesian network is small. Bayesian network classifiers outperform selective k-NN methods in terms of memory requirements and computational demands. This paper demonstrates the strength of Bayesian networks for classification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 38, Issue 1, January 2005, Pages 1-10
Journal: Pattern Recognition - Volume 38, Issue 1, January 2005, Pages 1-10
نویسندگان
Franz Pernkopf,