Genetic Algorithm Based Approach in Attribute Weighting for a Medical Data Set. (3rd September 2014)

• Record Type: Journal Article
• Authors: Varpa, Kirsi; Iltanen, Kati; Juhola, Martti
• Other Names: Murphy Martin J. Academic Editor.
• Is Part Of: Journal of computational medicine. Volume 2014(2014)
• Publication Date: 2014-09-03
• Subjects: Medical informatics -- Periodicals; Computational intelligence -- Periodicals; Computational intelligence; Medical informatics; Periodicals; Electronic journals; 610.285
• DOI: 10.1155/2014/526801
• Languages: English
• ISSNs: 2314-5080

Abstract

Abstract : 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.