A Hybrid Supervised ANN for Classification and Data Visualization

1. A Hybrid Supervised ANN for Classification and Data Visualization Chee Siong Teh and Md. Sarwar Zahan Tapan Presented by Jun-Yi Wu 2010/09/28 2008 IEEE

2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

3. Motivation • ANN usually do not support supervised classification and data visualization simultaneously.

4. Objectives • To propose a novel hybrid supervised ANN of Learning Vector Quantization(LVQ) with Adaptive Coordinate (AC) by hybridizing LVQ and modified AC approach.

5. Methodology A.Learning Vector Quantization(LVQ) B.Adaptive Coordinates(AC) C.Modified adaptation criteria of AC D.LVQ with AC Alogorithm E.Cost function of LVQ with AC

6. Methodology • Learning Vector Quantization(LVQ)

7. Methodology • Adaptive Coordinates(AC)

8. Methodology • Modified adaptation criteria of AC The proposed modification of AC is to ensure the integration of AC and LVQ enables the proposed method with data visualization.

9. Methodology • LVQ with AC Alogorithm

10. Methodology • Cost function of LVQ with AC

11. Experiments A.Visualization evaluation using a 3D synthetic data set B.Visualization evaluation using WBC data set C.Visualization evaluation using Wine data set D.Quantitative evaluation of LVQ with AC’s visualization E.Classification evaluation F.Network utilization and dead neuron

12. Experiments • Visualization evaluation using a 3D synthetic data set

13. Experiments

14. Experiments • Visualization evaluation using WBC data set

15. Experiments • Visualization evaluation using Wine data set

16. Experiments Quantitative evaluation of LVQ with AC’s visualization

17. Experiments Classification evaluation

18. Experiments Network utilization and dead neuron

19. Conclusion Empirical studies show that LVQwithAC be able to provide promising classification simultaneously with effective visualizations.

20. Comments • Advantages • map. Applications • Classification