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International Conference on Inductive Modelling, Kyiv 2008 Miroslav Cepek, 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).
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Automatic Method for Data Preprocessing for the GAME Inductive Modelling Method
Miroslav Č[email protected]
Miloslav Pavlicek, Pavel Kordik
Miroslav Šnorek
Computational Intelligence GroupDepartment of Computer Science and Engineering
Faculty of Electrical EngineeringCzech Technical University in Prague
ICIM 2008
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
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.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
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).
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
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.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
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.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
GA for Automatic Preprocessing Genetic algorithm goes in standard way as
shown below.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
GA Properties Selection – tournament selection
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.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
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.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
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.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
Artificial data
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
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.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
Best Chromosomes 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.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
Manually vs Automatically selected methods.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
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.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
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.
International Conference on Inductive Modelling, Kyiv 2008
Miroslav Cepek, [email protected]
Thank You for Your attention.