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

• Published in: 2009 International Conference on Machine Learning and Cybernetics Vol. 5; pp. 3070 - 3075
• Main Authors: Ping-Hung Tang, Ming-Hseng Tseng
• Format: Conference Proceeding
• Language: English
• 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
• DOI: 10.1109/ICMLC.2009.5212633
• ISBN: 9781424437023; 1424437024
• ISSN: 2160-133X

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.