Table of Contents
Journal of Computational Medicine
Volume 2014, Article ID 526801, 11 pages
Research Article

Genetic Algorithm Based Approach in Attribute Weighting for a Medical Data Set

Computer Science, School of Information Sciences, University of Tampere, 33014 Tampere, Finland

Received 28 May 2014; Revised 30 July 2014; Accepted 6 August 2014; Published 3 September 2014

Academic Editor: Martin J. Murphy

Copyright © 2014 Kirsi Varpa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Genetic algorithms have been utilized in many complex optimization and simulation tasks because of their powerful search method. In this research we studied whether the classification performance of the attribute weighted methods based on the nearest neighbour search can be improved when using the genetic algorithm in the evolution of attribute weighting. The attribute weights in the starting population were based on the weights set by the application area experts and machine learning methods instead of random weight setting. The genetic algorithm improved the total classification accuracy and the median true positive rate of the attribute weighted k-nearest neighbour method using neighbour’s class-based attribute weighting. With other methods, the changes after genetic algorithm were moderate.