A Balanced Ensemble Approach to Weighting Classifiers for Text Classification

1. A Balanced Ensemble Approach to Weighting Classifiers for Text Classification Advisor : Dr. Hsu Presenter : Yu-San Hsieh Author : Gabriel Pui Cheong Fung, Jeffrey Xu Yu, Haixun Wang, David W. Cheung, Huan Liu 2006. ICDM.1-5

2. Outline • Motivation • Objective • Method • Experiments • Conclusions

3. Motivation • Traditional ensemble approach didn’t consider that the classification performance is affected by three weight components • Global effectiveness • Local effectiveness • Decision confidence

4. Objective We proposed a new balanced combination function, called Dynamic Classifier Weighting (DCW), that incorporates the aforementioned three components.

5. Method Classifier 1 Classifier 2 Ensemble Ci di Classifier 3 ： Classifier n • Global effectiveness(αi) • Local effectiveness(βi) • Decision confidence (γi)

6. Experiments • Effectiveness Analysis • DCW performs inferior is case 6 • These results are all inferior than both WLC and our DCW < 0.77

7. Experiments • Significant Test ：a correct classification upon the ith unseen document ai = 1 : makes an correct one ai = 0 : makes an incorrect one da : the number of times that performs better than db : the number of times that performs better than hypothesis

8. Conclusions • The experimental result indicated that DCW can effectively balance the contribution of the three components and outperforms the exists approaches.

9. My opinion • Advantage • The approach is very simple. • Drawback • Experimental analysis • Application • Classification