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

(C) 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.

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

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

Medical Diagnosis: Diabetes

(C) 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

(C) SNU CSE BioIntelligence Lab 7 Report (1/4) Number of Epochs

(C) 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 

(C) SNU CSE BioIntelligence Lab 9 Report (3/4)

(C) 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 positive, otherwise negative.

(C) SNU CSE BioIntelligence Lab 11 Challenge (1) Unknown Data  Data for you: 3282 examples  16 dim-input vector labeled one of 5 classes  5 classes are: A,B, C, D, E Test data  582 examples  Labels are HIDDEN!

(C) SNU CSE BioIntelligence Lab 12 Challenge (2) Data  Train.txt : 3282 x 17 (16987 examples, 16 dim-input + with last column as label)  Test.txt: 582 x 16 (582 examples, 16 dim-input, labels are hidden) Verify your NN at 

(C) SNU CSE BioIntelligence Lab 13

(C) SNU CSE BioIntelligence Lab 14 A B C D E

(C) SNU CSE BioIntelligence Lab 15

(C) SNU CSE BioIntelligence Lab 16 제출할 것 최고 성능을 낸 제출자 명시 뉴럴넷 구조 최고 성능을 이끌어 내기 위해 자신이 시도한 내역 기술 자신의 최고 성능 (score) : 성능과 점수는 상관 관계가 작습니다.

(C) SNU CSE BioIntelligence Lab 17 References Source Codes  Free softwares  NN libraries (C, C++, JAVA, …)  MATLAB Tool box  Weka Web sites 

(C) SNU CSE BioIntelligence Lab 18 Pay Attention! Due (October 14, 2003): until pm 11:59 Submission  Results obtained from your experiments  Compress the data  Via  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

(C) SNU CSE BioIntelligence Lab 19 Optional Experiments Various learning rate Number of hidden layers Different k values Output encoding