Artificial Intelligence Project 1 Neural Networks
16
Artificial Intelligence Artificial Intelligence Project 1 Project 1 Neural Networks Neural Networks Biointelligence Lab School of Computer Sci. & Eng. Seoul National University
description
Artificial Intelligence Project 1 Neural Networks. Biointelligence Lab School of Computer Sci. & Eng. Seoul National University. Outline. Classification Problems Task 1 Estimate several statistics on Diabetes data set Task 2 - PowerPoint PPT Presentation
Transcript of Artificial Intelligence Project 1 Neural Networks
Artificial IntelligenceArtificial IntelligenceProject 1Project 1
Neural NetworksNeural Networks
Biointelligence Lab
School of Computer Sci. & Eng.
Seoul National University
(C) 2000-2002 SNU CSE BioIntelligence Lab
2
OutlineOutline
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 labels of test data are hidden.
(C) 2000-2002 SNU CSE BioIntelligence Lab
3
Network Structure (1)Network Structure (1)
…
positive
negative
fpos(x) > fneg(x),→ x is postive
(C) 2000-2002 SNU CSE BioIntelligence Lab
4
Network Structure (2)Network Structure (2)
…
f (x) > thres,→ x is postive
Medical Diagnosis: DiabetesMedical Diagnosis: Diabetes
(C) 2000-2002 SNU CSE BioIntelligence Lab
6
Pima Indian DiabetesPima 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/m2) Diabetes pedigree function Age (year)
Positive: 500, negative: 268
(C) 2000-2002 SNU CSE BioIntelligence Lab
7
Report (1/4)Report (1/4)
Number of Epochs
(C) 2000-2002 SNU CSE BioIntelligence Lab
8
Report (2/4)Report (2/4)
Number of Hidden Units At least, 10 runs for each setting
# Hidden
Units
Train Test
Average SD
Best Worst Average SD
Best Worst
Setting 1
Setting 2
Setting 3
(C) 2000-2002 SNU CSE BioIntelligence Lab
9
Report (3/4)Report (3/4)
(C) 2000-2002 SNU CSE BioIntelligence Lab
10
Report (4/4)Report (4/4)
Normalization method you applied. Other parameters setting
Learning rates Threshold value with which you predict an example as
positive. E.g. if f(x) > thres, you can say it is postive, otherwise negativ
e.
(C) 2000-2002 SNU CSE BioIntelligence Lab
11
Challenge (1)Challenge (1)
Unknown Data Data for you: 5822 examples Pos: 348, Neg: 5474
Test data 4000 examples Pos: 238, Neg: 3762 Labels are HIDDEN!
(C) 2000-2002 SNU CSE BioIntelligence Lab
12
Challenge (2)Challenge (2)
Data train.data : 5822 x 86 (5822 examples with 86 dim; labe
ls are attached at 86th-column: positive 1, negative 0) test.data: 4000 x 85 (5822 examples with 85 dim) Test labels are not given to you.
Verify your NN at http://knight.snu.ac.kr/aiproj1/ai_nn.asp
(C) 2000-2002 SNU CSE BioIntelligence Lab
13
Challenge (3)Challenge (3)
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
PredictPositive Negative
Positive
NegativeConfusion matrix
(C) 2000-2002 SNU CSE BioIntelligence Lab
14
ReferencesReferences
Source Codes Free softwares NN libraries (C, C++, JAVA, …) MATLAB Toolbox Weka
Web sites http://www.cs.waikato.ac.nz/~ml/weka/
(C) 2000-2002 SNU CSE BioIntelligence Lab
15
Pay Attention!Pay Attention!
Due (April 14, 2004): until pm 11:59 Submission
Results obtained from your experiments Compress the data Via e-mail ([email protected])
Report: printed version. (419 호 오장민 ) Used software and running environments Results for many experiments with various parameter settings Analysis and explanation about the results in your own way
메일 제목에 “ [4a05project1]” 반드시 포함
(C) 2000-2002 SNU CSE BioIntelligence Lab
16
Optional ExperimentsOptional Experiments
Various learning rate Number of hidden layers Applying feature selection techniques Output encoding
