In this paper, we propose a novel version of simple, yet effective Naïve Bayes classifier for mining streams. We add a weighting module, that automatically ...
Experimental analysis, carried out on a number of large data streams with concept drift, prove that the novel version of simple, yet effective Naïve Bayes ...
Weighted Naïve Bayes Classifier with Forgetting for Drifting Data ...
dl.acm.org › doi › SMC.2015.375
In this paper, we propose a novel version of simple, yet effective Naïve Bayes classifier for mining streams. We add a weighting module, that automatically ...
In [29] they have presented a novel and simple version which is indeed an effective Na¨ıve Bayes classifier for mining streaming data.
Weighted Naïve Bayes Classifier with Forgetting for Drifting Data Streams
www.kssk.pwr.edu.pl › paper
Weighted Naïve Bayes Classifier with Forgetting for Drifting Data Streams 2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015 ...
Bartosz Krawczyk, Michal Wozniak : Weighted Naïve Bayes Classifier with Forgetting for Drifting Data Streams. SMC 2015: 2147-2152. manage site settings.
In this paper we particularly focus on weighting variables for data streams for a naive Bayes classifier. This weighting produces a single model close to an “ ...
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In this study, we propose an online dynamic feature weighting algorithm. Specifically, a feature drift detection scheme is introduced that monitors the changes ...
Missing: Forgetting | Show results with:Forgetting
In this paper, a new Classifier based on hybrid approach is proposed that handle concept drifting stream data. The proposed classifier is used Naives Bayes as ...
Weighted Naïve Bayes Classifier with Forgetting for Drifting Data Streams · B. KrawczykMichal Wozniak. Computer Science. 2015 IEEE International Conference on ...