1. Artificial Intelligence Project 1 Neural Networks Biointelligence Lab School of Computer Sci. & Eng. Seoul National University

2. (C) 2000-2002 SNU CSE BioIntelligence Lab 2 Outline Classification Problems  Task 1  Estimate several statistics on Diabetes data set  Task 2  Given unknown data set, find the performance as good as you can get  The test data is hidden.

3. (C) 2000-2002 SNU CSE BioIntelligence Lab 3 Network Structure (1) … positive negative f pos (x) > f neg (x),→ x is postive

4. (C) 2000-2002 SNU CSE BioIntelligence Lab 4 Network Structure (2) … f (x) > thres,→ x is postive

5. Medical Diagnosis: Diabetes

6. (C) 2000-2002 SNU CSE BioIntelligence Lab 6 Pima Indian Diabetes Data (768)  8 Attributes  Number of times pregnant  Plasma glucose concentration in an oral glucose tolerance test  Diastolic blood pressure (mm/Hg)  Triceps skin fold thickness (mm)  2-hour serum insulin (mu U/ml)  Body mass index (kg/m 2 )  Diabetes pedigree function  Age (year)  Positive: 500, negative: 268

7. (C) 2000-2002 SNU CSE BioIntelligence Lab 7 Report (1/4) Number of Epochs

8. (C) 2000-2002 SNU CSE BioIntelligence Lab 8 Report (2/4) Number of Hidden Units  At least, 10 runs for each setting # Hidden Units TrainTest Average  SD BestWorst Average  SD BestWorst Setting 1 Setting 2 Setting 3 

9. (C) 2000-2002 SNU CSE BioIntelligence Lab 9 Report (3/4)

10. (C) 2000-2002 SNU CSE BioIntelligence Lab 10 Report (4/4) Normalization method you applied. Other parameters setting  Learning rates  Threshold value with which you predict an example as positive.  If f(x) > thres, you can say it is postive, otherwise negative.

11. (C) 2000-2002 SNU CSE BioIntelligence Lab 11 Challenge (1) Unknown Data  Data for you: 2000 examples  Pos: 1000, Neg: 1000 Test data  600 examples  Pos: 300, Neg: 300  Labels are HIDDEN!

12. (C) 2000-2002 SNU CSE BioIntelligence Lab 12 Challenge (2) Data  Train.data : 2000 x 500 (2000 examples with 500dim)  Train.labels: positive 1, negative 0  Test.data: 600 x 500 (600 examples with 500 dim)  Test.labels: not given to you. Verify your NN at  http://knight.snu.ac.kr/aiproj1/ai_nn_do.asp http://knight.snu.ac.kr/aiproj1/ai_nn_do.asp

13. (C) 2000-2002 SNU CSE BioIntelligence Lab 13 Challenge (3) K-fold Cross Validation  The data set is randomly divided into k subsets.  One of the k subsets is used as the test set and the other k-1 subsets are put together to form a training set. 200 D1D1 D2D2 D3D3 D8D8 D9D9 D 10 200 D1D1 D2D2 D3D3 D8D8 D9D9 D 10 200 D2D2 D3D3 D4D4 D8D8 D9D9 D 10 … … …

14. (C) 2000-2002 SNU CSE BioIntelligence Lab 14 Challenge (4) Include followings at your report  The best performance you achieved.  The spec of your NN when achieving the performance.  Structure of NN  Learning epochs  Your techniques  Other remarks… True Predict PositiveNegative Positive Negative Confusion matrix

15. (C) 2000-2002 SNU CSE BioIntelligence Lab 15 References Source Codes  Free softwares  NN libraries (C, C++, JAVA, …)  MATLAB Tool box  Weka Web sites  http://www.cs.waikato.ac.nz/~ml/weka/

16. (C) 2000-2002 SNU CSE BioIntelligence Lab 16 Pay Attention! Due (October 14, 2004): until pm 11:59 Submission  Results obtained from your experiments  Compress the data  Via e-mail  Report: Hardcopy!!  Used software and running environments  Results for many experiments with various parameter settings  Analysis and explanation about the results in your own way

17. (C) 2000-2002 SNU CSE BioIntelligence Lab 17 Optional Experiments Various learning rate Number of hidden layers Different k values Output encoding