Using Unlabeled Data to Improve Classification in the Naive Bayes Approach: Application to Web Searc
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WPnull/02 Using Unlabeled Data to Improve Classification in the Naive Bayes Approach: Application to Web Searc
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Authors
- Stella M. Salvatierra(ssalvat@unav.es)
School of Economics and Business Administration, University of Navarra
Abstract
This paper introduces a method to build a classifier based on labeled and unlabeled data. We set up the EM algorithm steps for the particular case of the naive Bayes approach and show empirical work for the restricted web page database. Original contributions includes the application of the EM algorithm to simulated data in order to see the behavior of the algorithm for different numbers of labeled and unlabeled data, and to study the effect of the sampling mechanism for the unlabeled data on the results.
JEL Classification:C11; C13; C15; C49
Number of Pages:15
Creation Date:2002-10-01
Number:null/02
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