Chapter IX: Extracting Knowledge from Databases and ANNs with Genetic Programming - Iris Flower Classification Problem

Daniel Rivero, University of A Coru ±a, Spain
Juan R. Rabu ±al, University of A Coru ±a, Spain
Juli n Dorado, University of A Coru ±a, Spain
Alejandro Pazos, University of A Coru ±a, Spain
Nieves Pedreira, University of A Coru ±a, Spain

In this chapter, we present an application of Genetic Programming (GP) in the field of data mining and extraction of Artificial Neural Networks (ANN) rules. To do this, we will use its syntactic properties to obtain high level expressions that represent knowledge. These expressions will have different types as there is the need at each moment: we will obtain different expressions like IF-THEN-ELSE rules, mathematical relations between variables or boolean expressions. In this chapter, we will not only apply GP to solve the problem, but we will try different modifications and different ways to apply it to solve the problem. We will show how making a data pre-processing we can obtain better results than using the original values. That is, by adding a little knowledge from the problem we can improve the performance of GP.

INTRODUCTION

In the world of Artificial Intelligence (AI), the extraction of knowledge has been a very useful tool for many different purposes, and it has been tried with many different techniques. Here, we will show how we can use Genetic Programming (GP) to solve a classification problem from a database, and we will show how we can adapt this tool in two different ways: to improve its performance and to make it possible to detect errors. Results show that the technique developed in this chapter opens a new area for research in the field, extracting knowledge from more complicated structures such as Artificial Neural Networks (ANNs).