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CSCI 548/B480: Introduction to Bioinformatics Fall 2002 Dr. Jeffrey Huang, Assistant Professor Department of Computer and Information Science, IUPUI E-mail: huang@cs.iupui.edu Topic 5: Machine Intelligence - Learning and Evolution
Machine Intelligence ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Behavior-Based AI vs. Knowledge Based ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Environment percepts actions sensors effectors agent ?
Operational Agents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Building Intelligent Artifacts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],Classification vs. Prediction
Classification—A Two-Step Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification Process Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’ Model Construction Use the Model in Prediction (Jeff, Professor, 2) Tenured? Training Data Classifier (Model) Testing Data Unseen Data Classifier (Model)
Supervised vs. Unsupervised Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification and Prediction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
From Learning to Evolutionary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
The Essence Components ,[object Object],[object Object],[object Object],[object Object],[object Object]
Evolutionary Algorithm Search Procedure Randomly generate an initial population M(0) Compute and save the fitness u(m) for each individual m in the current population M(t) Define selection probabilities p(m) for each individual m in M(t) so that p(m) is proportional to u(m) Generate M(t+1) by probabilitically selecting individuals to produce offspring via genetic operations ( Crossover and mutation )
Historical Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Examples of Evolutionary AI ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How Does Genetic Algorithm Work? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],00000000000000 00000000000001 … … 11111111111111 0.0 4/(2 14 -1) … … 4.0 genotype Phenotype v 1 v 2 v 3 v 4 v 5 v 6 v 7 v 8 v 9 v 10 v 11 v 12 v 13 v 14 v 15 v 16 v 17 v 18 v 19 v 20 v 21 v 22 v 23 v 24
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Homing to the Optimal Solution
Best-so-far Curve
Optimal Feature Subset ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inductive Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification by Decision Tree Induction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],6 5 4 3 2 1 Exs. Smooth Yellow Medium B Smooth Yellow Medium B Rough Red Big A Smooth Red Medium A Smooth Red Medium A Smooth Yellow Small A Surface Color Size Class color yellow red A size small medium B A
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object]
Decision Tree Induction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object]
The ID3 Algorithm and Quinlan’s C4.5 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],6 5 4 3 2 1 Exs. Smooth Yellow Medium B Smooth Yellow Medium B Rough Red Big A Smooth Red Medium A Smooth Red Medium A Smooth Yellow Small A Surface Color Size Class color yellow red A size small medium B A color yellow red A ?
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extracting Classification Rules from Trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Decision Tree ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Decision Tree ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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WEB DESIGN!
 

Topic_6

  • 1. CSCI 548/B480: Introduction to Bioinformatics Fall 2002 Dr. Jeffrey Huang, Assistant Professor Department of Computer and Information Science, IUPUI E-mail: huang@cs.iupui.edu Topic 5: Machine Intelligence - Learning and Evolution
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  • 9. Classification Process Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’ Model Construction Use the Model in Prediction (Jeff, Professor, 2) Tenured? Training Data Classifier (Model) Testing Data Unseen Data Classifier (Model)
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  • 15. Evolutionary Algorithm Search Procedure Randomly generate an initial population M(0) Compute and save the fitness u(m) for each individual m in the current population M(t) Define selection probabilities p(m) for each individual m in M(t) so that p(m) is proportional to u(m) Generate M(t+1) by probabilitically selecting individuals to produce offspring via genetic operations ( Crossover and mutation )
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  • 24. Homing to the Optimal Solution
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