A decision tree-based attribute weighting filter for naive Bayes
نویسندگان
چکیده
منابع مشابه
A decision tree-based attribute weighting filter for naive Bayes
The naive Bayes classifier continues to be a popular learning algorithm for data mining applications due to its simplicity and linear run-time. Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness—the assumption that attributes are independent given the class. All of them improve the performance of naive Bayes at the expense (to a greater or lesser d...
متن کاملAlleviating naive Bayes attribute independence assumption by attribute weighting
Despite the simplicity of the Naive Bayes classifier, it has continued to perform well against more sophisticated newcomers and has remained, therefore, of great interest to the machine learning community. Of numerous approaches to refining the naive Bayes classifier, attribute weighting has received less attention than it warrants. Most approaches, perhaps influenced by attribute weighting in ...
متن کاملSelf-adaptive attribute weighting for Naive Bayes classification
http://dx.doi.org/10.1016/j.eswa.2014.09.019 0957-4174/ 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel./fax: +86 27 67883714. E-mail addresses: [email protected] (J. Wu), [email protected]. edu.au (S. Pan), [email protected] (X. Zhu), [email protected] (Z. Cai), peng.zhang@uts. edu.au (P. Zhang), [email protected] (C. Zhang). Jia Wu , Shirui Pan , Xingquan Zh...
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The naive Bayes (NB) is a popular classification technique for data mining and machine learning, which is based on the attribute independence assumption. Researchers have proposed out many effective methods to improve the performance of NB by lowering its primary weakness---the assumption that attributes are independent given the class, such as backwards sequential elimination method, lazy elim...
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In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The ...
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ژورنال
عنوان ژورنال: Knowledge-Based Systems
سال: 2007
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2006.11.008