JKS – Seminar Talk

1. JKS – Seminar Talk Yanjun Qi 2005.Nov.14

2. Outline • Mixture of Experts • Feature Experts • Performance • Expression expert – useful or not?

3. Split our feature sets into four sets  call them as expert Y Mixture of Experts expertP expertE expertS expertF

4. X M Y Mixture of Experts • Root Gate is input dependent • Weight trained by EM • Each expert’s parameter also trained by EM

5. Four feature experts

6. Performance Comparison • logistic regression • Naïve bayes • Random forest • Support vector machine • Mixture of 4 feature experts

7. Performance Comparison – AUC score

8. Performance Comparison – PreCal curve

9. Expression expert – useful or not? • Due to the last expression expert did not contribute much from its single performance, • Found to be most important in RF Gini • So we change this expert to the full expert to see how performance changes or delete this expert to see the whole performance change.

10. Expression expert – useful or not? •  if we change the last expression expert to the full data, the performance get a little better •  if we remove the last expression expert, the performance does not affect much

11. Delete Expert in turn • From the above comparison, we then want to see how performance changes if we delete one of the other three experts

12. Delete Expert in turn

13. RF based feature GINI importance measure for the experts

14. Expression expert – useful or not? •  Expression experts related features would be useful when combining with others •  It is not predictive for the PPI task alone

15. Judith Klein-Seetharaman. Ziv Bar-Joseph Acknowledge