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Extracting Initial and Reliable Negative Documents to Enhance Classification Performance.
- Source :
- Knowledge Discovery in Life Science Literature; 2006, p104-111, 8p
- Publication Year :
- 2006
-
Abstract
- Most existing text classification work assumes that training data are completely labeled. In real life, some information retrieval problems can only be described as learning a binary classifier from a set of incompletely labeled examples, where a small set of labeled positive examples and a very large set of unlabeled ones are provided. In this case, all of the traditional text classification methods can't work properly. In this paper, we propose a method called Weighted Voting Classifier, which is an improved 1-DNF algorithm. Experimental results on the Reuters-21578 set show that our algorithm Weighting Voting Classifier outperforms PEBL and one-class SVM in terms of F measure. Weighting Voting Classifier can achieve high F score when comparing with PEBL and one-class SVM. Furthermore, the reduction of iterations is 2.26 when comparing the method of PEBL with ours. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540328094
- Database :
- Supplemental Index
- Journal :
- Knowledge Discovery in Life Science Literature
- Publication Type :
- Book
- Accession number :
- 32889137
- Full Text :
- https://doi.org/10.1007/11683568_9