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What is Unlabeled Set

Handbook of Research on Text and Web Mining Technologies
A set U of the mixed set which contains both hidden positive documents from class P and also hidden negative documents. Unlabeled set U, when combined with positive set P, is useful for building a classifier to identify novel positive documents.
Published in Chapter:
Partially Supervised Text Categorization
Xiao-Li Li (Institute for Infocomm Research, A* STAR, Singapore)
Copyright: © 2009 |Pages: 21
DOI: 10.4018/978-1-59904-990-8.ch005
Abstract
In traditional text categorization, a classifier is built using labeled training documents from a set of predefined classes. This chapter studies a different problem: partially supervised text categorization. Given a set P of positive documents of a particular class and a set U of unlabeled documents (which contains both hidden positive and hidden negative documents), we build a classifier using P and U to classify the data in U as well as future test data. The key feature of this problem is that there is no labeled negative document, which makes traditional text classification techniques inapplicable. In this chapter, we introduce the main techniques S-EM, PEBL, Roc-SVM and A-EM, to solve the partially supervised problem. In many application domains, partially supervised text categorization is preferred since it saves on the labor-intensive effort of manual labeling of negative documents.
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Machine Learning Approach to Search Query Classification
Set of examples whose labels or classes are unknown. If the class of an unlabeled example is learned, it can then be added to a “labeled set”.
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