Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/114659
Title: Learning to classify texts using positive and unlabeled data
Authors: Li, X. 
Liu, B.
Issue Date: 2003
Citation: Li, X.,Liu, B. (2003). Learning to classify texts using positive and unlabeled data. IJCAI International Joint Conference on Artificial Intelligence : 587-592. ScholarBank@NUS Repository.
Abstract: In traditional text classification, a classifier is built using labeled training documents of every class. This paper studies a different problem. Given a set P of documents of a particular class (called positive class) and a set U of unlabeled documents that contains documents from class P and also other types of documents (called negative class documents), we want to build a classifier to classify the documents in U into documents from P and documents not from P. The key feature of this problem is that there is no labeled negative document, which makes traditional text classification techniques inapplicable. In this paper, we propose an effective technique to solve the problem. It combines the Rocchio method and the SVM technique for classifier building. Experimental results show that the new method outperforms existing methods significantly.
Source Title: IJCAI International Joint Conference on Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/114659
ISSN: 10450823
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

46
checked on Aug 16, 2019

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.