Database Analysis with ANNs by means of Graph Evolution-Reference-Cited by-同舟云学术

Database Analysis with ANNs by means of Graph Evolution

Published:2013 Issue: Volume: Page:704-718
ISSN:
Container-title:Data Mining
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Author:

Rivero Daniel¹,Dorado Julián¹,Rabuñal Juan¹,Pazos Alejandro¹

Affiliation:

1. University of A Coruña, Spain

Abstract

Traditionally, the development of Artificial Neural Networks (ANNs) is a slow process guided by the expert knowledge. This expert usually has to test several architectures until he finds one suitable for solving a specific problem. This makes the development of ANNs a slow process in which the expert has to do much effort. This chapter describes a new method for the development of Artificial Neural Networks, so it becomes completely automated. Since ANNs are complex structures with very high connectivity, traditional algorithms are not suitable to represent them. For this reason, in this work graphs with high connectivity that represent ANNs are evolved. In order to measure the performance of the system and to compare the results with other ANN development methods by means of Evolutionary Computation (EC) techniques, several tests were performed with problems based on some of the most used test databases in Data Mining. These comparisons show that the system achieves good results that are not only comparable to those of the already existing techniques but, in most cases, improve them.

Publisher

IGI Global

Reference32 articles.

1. Alba, E., Aldana, J. F., & Troya, J. M. (1993). Fully automatic ANN design: A genetic approach. In Proc. Int. Workshop Artificial Neural Networks (IWANN’93), (LNCS 686, pp. 399-404). Berlin: Springer-Verlag.

2. Belew, R., McInerney, J., & Schraudolph, N. (1991). Evolving networks: using the genetic algorithm with connectionist learning. In Proceedings of the Second Artificial Life Conference, (pp. 511-547). New York: Addison-Wesley.

3. An Empirical Comparison of Combinations of Evolutionary Algorithms and Neural Networks for Classification Problems

4. DasGupta, B. & Schnitger, G. (1992). Efficient approximation with neural networks: A comparison of gate functions. Dep. Comput. Sci., Pennsylvania State Univ., University Park, Tech. Rep.