Medical data mining using BGA and RGA for weighting of features in fuzzy k-NN classification

The k-nearest neighbor (k-NN) algorithm is commonly used in applications of classifiers and data mining and the related area due to its simplicity and effectiveness. In this study, all of features and optimal feature subsets with three features are investigated. For classification, crisp k-NN, fuzzy...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 5; pp. 3070 - 3075
Main Authors Ping-Hung Tang, Ming-Hseng Tseng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Binary-coded genetic algorithms
Breast cancer
Cancer detection
Crisp k-NN
Cybernetics
Data mining
Diabetes
Electronic mail
Fuzzy k-NN
Genetic algorithms
Gradient methods
Machine learning
Medical diagnostic imaging
Real-coded genetic algorithms
Weighting fuzzy k-NN
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Summary:The k-nearest neighbor (k-NN) algorithm is commonly used in applications of classifiers and data mining and the related area due to its simplicity and effectiveness. In this study, all of features and optimal feature subsets with three features are investigated. For classification, crisp k-NN, fuzzy k-NN, and weighting fuzzy k-NN classifiers are compared. For weighting of features, two types of coding including binary-coded genetic algorithms (BGA) and real-coded genetic algorithms (RGA) are evaluated. Experiments are conducted on the Wisconsin diagnosis breast cancer (WDBC) dataset and the Pima (PIMA) Indians diabetes dataset, and the classification accuracy, false negative, and computation time are reported in this paper.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212633