New Book:
Web Data Mining - Exploring Hyperlinks,
Contents and Usage Data New Book:
Web Data Mining - Exploring Hyperlinks,
Contents and Usage Data Funded by: NSF (National Science Fundation)
Award No: IIS-0307239
Recent talk given at Boeing, UIUC, University of Notre Dame, University of
Trento (Italy) and University of Siena (Italy) which summarizes the theory and some algorithms.
Text classification is an important problem that has numerous applications. It is commonly stated as follows: Given a set of labeled training documents of n classes, the system uses this training set to build a classifier, which is then used to classify new documents into the n classes. Although this classic model is important, in practice one also encounters another problem. That is, one has a set of documents of a particular topic or class P (positive class), and is given a large set U of mixed (unlabelled) documents that contains documents from class P and also other types of documents (negative documents). One wants 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 training data, which makes the traditional text classification techniques inapplicable. This problem is termed, partially supervised classification (PSC). We also call it PU-learning (Learning from Positive and Unlabeled examples).
The objectives of this project are to design a robust and principled technique to solve PSC, implement a system for PSC, devise a method to evaluate such techniques, and identify methods for determining the minimum number of labeled documents needed to achieve the optimal accuracy in order to reduce manual labeling efforts. The results of this research should be widely useful because the identification of targeted information/documents is of great value in this information age.
In our work in (Liu et al. 2002), it was shown theoretically that P and U provide sufficient information for learning, and the problem can be posed as a constrained optimization problem. This theoretical result provides a good guidance for designing practical algorithems. Some of our algorithms are reported in (Liu et al 2003), (Lee and Liu 2003) and (Li and Liu 2003). Since research in this direction only started recently, many important issues still need
to be addressed in order to gain a better understanding of the problem.
Download our latest
LPU system, including help information
NSF Grant Report, Aug 5, 2003. IDM 2003 Workshop, September 14-16, 2003, Seattle, Washington.
Created on July 20, 2003 by Bing Liu.