A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm

k Nearest neighbor, Bayesian methods and genetic algorithms are effective methods of machine learning. In this work a hybrid method is formed by using these methods and algorithm together. The aim is to achieve successful results on classifying by eliminating data that make difficult to learn. Formi...

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Bibliographic Details
Published in:Expert systems with applications 2010-07, Vol.37 (7), p.5061-5067
Main Authors: Aci, Mehmet, İnan, Cigdem, Avci, Mutlu
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Bayesian method
Cancer
Classification
Clustering
Expert systems
Genetic algorithm
Genetic algorithms
Hybrid method
k Nearest neighbor method
Machine learning
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Summary:k Nearest neighbor, Bayesian methods and genetic algorithms are effective methods of machine learning. In this work a hybrid method is formed by using these methods and algorithm together. The aim is to achieve successful results on classifying by eliminating data that make difficult to learn. Forming new data set approach is proposed according to good data on the hand. Test process is done with five of UCI machine learning datasets. These are iris, breast cancer, glass, yeast and wine data sets. Test results are investigated in collaboration with the previous works, and the success of the study is considered.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2009.12.004