artificial intelligence project 1 neural networks
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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 PresentationTRANSCRIPT
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
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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.
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Network Structure (1)Network Structure (1)
…
positive
negative
fpos(x) > fneg(x),→ x is postive
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Network Structure (2)Network Structure (2)
…
f (x) > thres,→ x is postive
Medical Diagnosis: DiabetesMedical Diagnosis: Diabetes
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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
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Report (1/4)Report (1/4)
Number of Epochs
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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
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Report (3/4)Report (3/4)
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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.
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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!
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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
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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
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ReferencesReferences
Source Codes Free softwares NN libraries (C, C++, JAVA, …) MATLAB Toolbox Weka
Web sites http://www.cs.waikato.ac.nz/~ml/weka/
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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
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Optional ExperimentsOptional Experiments
Various learning rate Number of hidden layers Applying feature selection techniques Output encoding