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

Content Provider	IEEE Xplore Digital Library
Author	Ping-Hung Tang Ming-Hseng Tseng
Copyright Year	2009
Description	Author affiliation: Graduate Institute of Applied Information Sciences, Chung-Shan Medical University, China (Ping-Hung Tang) \|\| School of Applied Information Sciences, Chung-Shan Medical University, China (Ming-Hseng Tseng)
Abstract	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 (BGA) 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.
Starting Page	3070
Ending Page	3075
File Size	253259
Page Count	6
File Format	PDF
ISBN	9781424437023
DOI	10.1109/ICMLC.2009.5212633
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Medical diagnostic imaging Genetic algorithms Machine learning Cybernetics Breast cancer Diabetes Electronic mail Gradient methods Cancer detection Real-coded genetic algorithms Crisp k-NN Fuzzy k-NN Weighting fuzzy k-NN Binary-coded genetic algorithms
Content Type	Text
Resource Type	Article

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