Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem

  • Daniel Rivero
  • Juan Rabuñal
  • Julián Dorado
  • Alejandro Pazos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)


This paper describes a new technique for automatically developing Artificial Neural Networks (ANNs) by means of an Evolutionary Computation (EC) tool, called Genetic Programming (GP). This paper also describes a practical application in the field of Data Mining. This application is the Iris flower classification problem. This problem has already been extensively studied with other techniques, and therefore this allows the comparison with other tools. Results show how this technique improves the results obtained with other techniques. Moreover, the obtained networks are simpler than the existing ones, with a lower number of hidden neurons and connections, and the additional advantage that there has been a discrimination of the input variables. As it is explained in the text, this variable discrimination gives new knowledge to the problem, since now it is possible to know which variables are important to achieve good results.


Genetic Program Hide Neuron Automatic Design Sepal Length Data Mining Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Daniel Rivero
    • 1
  • Juan Rabuñal
    • 1
  • Julián Dorado
    • 1
  • Alejandro Pazos
    • 1
  1. 1.Department of Information & Communications Technologies, Campus Elviña, 15071, A CoruñaSpain

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