1. GATree Genetically Evolved Decision Trees
Papagelis Athanasios - Kalles Dimitrios Computer Technology Institute

2. Introduction
We use GA’s to evolve simple and accurate binary decision trees
Simple genetic operators over tree structures
Experiments with UCI datasets
very good size
competitive accuracy results

3. Why it should work ? GA’s are not They are … Hill climbers
Blind on complex search spaces
Exhaustive searchers
Extremely expensive
They are …
Beam searchers
They balance between time needed and space searched

4. The question…
Are there datasets where hill-climbing techniques are really inadequate ?
e.g unnecessary big – misguiding output
Yes there are…
Conditionally dependent attributes
e.g XOR
Irrelevant attributes
Many solutions that use GAs as a preprocessor so as to select adequate attributes
Direct genetic search can be proven more efficient for those datasets

5. The proposed solution
Select the desired decision tree characteristics (e.g small size)
Create an appropriate fitness function
Adopt a decision tree representation with appropriate genetic operators
Evolve for as long as you wish!

6. Genetic operators

7. Payoff function Balance between accuracy and size
set x depending on the desired output characteristics.
Small Trees ?  x near one
Emphasis on accuracy ?  x grows big

8. Results

9. Future work Minimize evolution time
Improved node statistics
Choose the output class using a majority vote over the produced tree forest
Dynamic tuning of initial parameters
Experiments with synthetic datasets
Specific characteristics