Automatic Method for Data Preprocessing for the GAME Inductive Modelling Method

1. Automatic Method for Data Preprocessing for the GAME Inductive Modelling Method Miroslav Čepek cepekm1@fel.cvut.cz Miloslav Pavlicek, Pavel Kordik Miroslav Šnorek Computational Intelligence Group Department of Computer Science and Engineering Faculty of Electrical Engineering Czech Technical University in Prague ICIM 2008

2. Automatic preprocessing • The GAME Neural Network (as all others data mining methods) heavily depends on data preprocessing. • Preprocessing involves selection, setup and ordering of preprocessing methods. • We want to automate this stage. • We will use genetic algorithm to find optimal sequence of methods.

3. GAME Neural Network • Group of Adaptive Method Evolution (GAME) uses inductive modelling. • The structure of the model is created in inductive way (data driven modelling).

4. Main Ideas of Automatic Preprocessing • The main idea is to use genetic algorithms to find optimal order and optimal setup of data preprocessing methods. • In the first stage we will to use simple genetic algorithm. • Because we want to find sequence which will the most fits the GAME ANN we will use reduced GAME ANN for fitness function evaluation.

5. Single individual in automatic preprocessing • The individuals in our automatic consists of list of preprocessing methods. • Each method can be applied to different attributes. • Each method have different setup. • Methods are applied one by one. • Some methods changes structure of the dataset (PCA) and must be treated separately.

6. GA for Automatic Preprocessing • Geneticalgorithmgoes in standard wayasshownbelow.

7. GA Properties • Selection – tournamentselection • Several individuals are selected at random from population and individual with the highest fitness is selected. • Cross over – standard one-point cross over. • Mutation • adds or removes preprocessing methods from individual. • changes order of methods. • changes configuration of methods.

8. Fitness Recalculation • Fitness is average accuracy of several simple GAME models generated from data preprocessed by given individual. • Accuracy of models is not always the same due to genetic algorithm involved in training. • Using several models allows more consistent results. • We assume that better simple model also means better complex models.

9. Outline of the Experiment • Complete dataset is split into training and testing part. • From training data given portion of values is removed. • Several GAME models are created on raw data. • Instances with missing values are removed. Then several GAME models are created. • Automatic preprocessing is performed. The best individual is selected and preprocessing methods are applied and several GAME models are created.

10. Artificial data

11. Best Chromosomes The best individuals for selected amount of missing values. Part a) shows the best chromosome 1% of missing values. Part b) shows individual for 5% of missing values and c) shows 20% of missing values.

12. BestChromosomes • Chromosomes for simple problems (low number of missing values) are quite simple. • Chromosomes for complicated problems (high number of missing values) are quite complicated. • In this sense our algorithm works.

13. Manually vs Automatically selected methods.

14. Results • Graph shows that GAME is unable to handle missing values. Results of RAW data are quite poor. • When instances with missing data are removed, accuracy increase rapidly. • When automatic preprocessing is used accuracy is even better.

15. Conclusion • We proposed algorithm for automatic selection and ordering of data preprocessing methods. • We performed the first experiment with our method. • It works for artificial data and in future we have to prove that it work also for more complicated and real-world data.

16. Thank You for Your attention. cepekm1@fel.cvut.cz