1. Fuzzy Final Homework System Implementation Selected paper: Fuzzy integration of structure adaptive SOMs for web content mining, Fuzzy Sets and Systems 148 (2004) 43–60 Lecture: Prof. Hahn-Ming Lee Student: Ching-Hao Mao D9415004@mail.ntust.edu.tw

2. Outline Introduction Proposed method in selected paper Implementation Conclusion References

3. Introduction In this report, we implement Kim and Cho’s paper appear on Fuzzy Set and System in 2004 User profile represents different aspects of user’s characteristics The author proposed an ensemble of classifiers that estimate user’s preference using web content labeled by user as “like” or “dislike”

4. Introduction- Preview Studies [2]

5. Feature Selection Method Properties Feature selection methods such as Information Gain, TFIDF, and ODDS ratio have different properties TFIDF does not consider class values of documents when calculating the relevance of features while information gain uses class labels of documents Odds ratio uses class labels of documents but they find useful features to classify only one specific class

6. Overview of the proposed method in [1] Classification TFIDF, Information Gain, ODDS Ratio

7. Structure Adaptive SOM

8. Training SASOM ’ s using different feature sets Fuzzy Integral Hot Cold or

9. Data Set Description UCI Syskill & Webert data (http://kdd.ics.uci.edu)http://kdd.ics.uci.edu Contain the HTML source of web pages plus the ratings of a single user on these web pages The web pages are on four separate subjects Bands- recording artists (Implement in this report) Goats (Implement in this report) Sheep BioMedical

10. Implementation Coding Java (J2SE 1.5) program for preprocessing, feature selection (TFIDF and ODDS Ratio), and Fuzzy Integral mechanism Using Weka for Feature Selection (Information Gain) and Classification This report not successfully program SASOM…

11. Implementation-preprocessing UCI Syskill & Webert data ExtractHTMLContent.java Pure Text without Anchor Text Bands.txt After Stopword and Porter Stemmer Bands_Stopword.txt Bands_Porter.txt

12. Implementation- Feature Selection In Bands, 61 dataset E.g. Attribute Number: 5436->32 Information GainTFIDFODDS Ratio 0.1435 1411 mother 0.1435 4109 writes 0.1054 49 places 0.1054 855 letter 0.1054 3883 movement 0.1054 1464 stories 0.1054 3856 synthesizer 0.1054 2568 songwriters 0.0962 4643 singer 0.0937 50 america sea acid programming innovative letter method members bleed concentrated mother oss wild cultures vehemently smoking define book charge library hand

13. Implementation- Fuzzy Integral Fuzzy measure of classifiers that are determined subjectively [1] Bayes Classifier b1,b2,b3 b1=0, b2=1, b3=0 0.99 FuzzyIntegral.java (g1,g2,g3) 0.99,0.99,0.99)(0.01,0.01,0.99) (b1,b2,b3)Result(b1,b2,b3)Result (0,1,0)0.99(0,0,1)0.01 (1,1,1)0.99(0,1,1)0.01 (0,0,0)0,99(0,0,0)0.01

14. Conclusion Fuzzy integral provides the method of measuring the importance of classifiers subjectively, especially in semi-supervised learning method The method based on fuzzy integral can be effectively applied to web content mining for predicting user’s preference as user profile Fuzzy Integral maybe can apply into my research area to integrate expert or user’s knowledge

15. References 1. Kyung-Joong Kim, Sung-Bae Cho, Fuzzy integration of structure adaptive SOMs for web content mining, Fuzzy Sets and Systems 148 (2004) 43–60 2. Pazzani M., Billsus, D., Learning and Revising User Profiles: The identification of interesting web sites, Machine Learning 27 (1997), 313- 331 3. http://kdd.ics.uci.edu/databases/SyskillWebert/SyskillWebert.data.html