1. PEBL: Web Page Classification without Negative Examples
Hwanjo Yu, Jiawei Han, and Kevin Chen-Chuan
IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 1, 2004
Presented by Chirayu Wongchokprasitti

2. Introduction
Web page classification is one of the main techniques for Web mining
Constructing a classifier requires positive and negative training examples
Cautious to avoid bias and laborious to collect negative training examples

3. Typical Learning Framework

4. Positive Example Base Learning (PEBL) Framework
Learn from positive data and unlabeled data
Unlabeled data indicates random samples of the universal set
Apply the Mapping-Convergence (M-C) Algorithm

5. Mapping-Convergence (M-C) Algorithm
Divide into 2 stages
Mapping stage
Use any classifier that does not generate false negatives
They chose 1-DNF ( monotone Disjunctive Normal Form)
Convergence stage
For maximizing margin
They chose SVM (Support Vector Machine)

6. Mapping Stage
Use a weak classifier to draw an initial approximation of “strong” negative data.
First, Identify strong positive features from positive and unlabeled data by checking the frequency of those features.
If feature frequency in positive data is larger than one in the universal data, it is a strong positive
Filter out any possible positive, leaving only strong negatives.

7. Convergence Stage Use SVM to scope down the class boundary
Iterate SVM for certain times to extract negative data from unlabeled data
The boundary will converge into the true boundary.

8. Support Vector Machines
Visualization of a Support Vector Machine

9. Convergence of SVM

10. Data Flow Diagram

11. Experimental Results
Report the result with precision-recall breakeven point (P-R)
Experiment 1: the Internet
Use DMOZ as the universal set
Experiment 2: University CS department
Use WebKB data set
Mixture Models

12. Experiment 1

13. Experiment 2

14. Mixture Models

15. Summary and Conclusions
PEBL framework eliminates the need for manually collecting negative training examples
The Mapping-Convergence (M-C) algorithm achieves classification accuracy as high as that of traditional SVM
PEBL needs faster training time