Editing for the k-nearest neighbors rule by a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm
Pattern Recognition Letters
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A study of crossover operators for gene selection of microarray data
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Nonlinear parameter estimation via the genetic algorithm
IEEE Transactions on Signal Processing
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Merge method for shape-based clustering in time series microarray analysis
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Recently, microarray technology has widely used on the study of gene expression in cancer diagnosis. The main distinguishing feature of microarray technology is that can measure thousands of genes at the same time. In the past, researchers always used parametric statistical methods to find the significant genes. However, microarray data often cannot obey some of the assumptions of parametric statistical methods, or type I error may be over expanded. Therefore, our aim is to establish a gene selection method without assumption restriction to reduce the dimension of the data set. In our study, adaptive genetic algorithm/k-nearest neighbor (AGA/KNN) was used to evolve gene subsets. We find that AGA/KNN can reduce the dimension of the data set, and all test samples can be classified correctly. In addition, the accuracy of AGA/KNN is higher than that of GA/KNN, and it only takes half the CPU time of GA/KNN. After using the proposed method, biologists can identify the relevant genes efficiently from the sub-gene set and classify the test samples correctly.