Using Unlabeled Data to Improve Classification in the Naive Bayes Approach: Application to Web Searc

Stella Salvatierra ()

No 06/02, Faculty Working Papers from 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-codes: C11 C13 C15 C49 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2002-10-01
References: View complete reference list from CitEc
Citations:

Forthcoming, Journal of Computational Methods for Science and Engineering

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