An introduction to genetic algorithms
An introduction to genetic algorithms
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data mining in bioinformatics using Weka
Bioinformatics
Experimental study for the comparison of classifier combination methods
Pattern Recognition
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Improved binary PSO for feature selection using gene expression data
Computational Biology and Chemistry
A review of feature selection techniques in bioinformatics
Bioinformatics
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
On optimum choice of k in nearest neighbor classification
Computational Statistics & Data Analysis
Computers in Biology and Medicine
Decision forest for classification of gene expression data
Computers in Biology and Medicine
Gene expression data classification using locally linear discriminant embedding
Computers in Biology and Medicine
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
An Evolutionary Algorithm Approach to Optimal Ensemble Classifiers for DNA Microarray Data Analysis
IEEE Transactions on Evolutionary Computation
Recursive Fuzzy Granulation for Gene Subsets Extraction and Cancer Classification
IEEE Transactions on Information Technology in Biomedicine
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Hybridising harmony search with a Markov blanket for gene selection problems
Information Sciences: an International Journal
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Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. In cancer classification, available training data sets are generally of a fairly small sample size compared to the number of genes involved. Along with training data limitations, this constitutes a challenge to certain classification methods. Feature (gene) selection can be used to successfully extract those genes that directly influence classification accuracy and to eliminate genes which have no influence on it. This significantly improves calculation performance and classification accuracy. In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined into a hybrid method, and the K-nearest neighbor (KNN) with the leave-one-out cross-validation (LOOCV) method served as a classifier for eleven classification profiles to calculate the classification accuracy. Experimental results show that the proposed method reduced redundant features effectively and achieved superior classification accuracy. The classification accuracy obtained by the proposed method was higher in ten out of the eleven gene expression data set test problems when compared to other classification methods from the literature.