An O(n log n) algorithm for the all-nearest-neighbors problem
Discrete & Computational Geometry
Machine Learning
Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
An introduction to variable and feature selection
The Journal of Machine Learning Research
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining in bioinformatics using Weka
Bioinformatics
Experimental study for the comparison of classifier combination methods
Pattern Recognition
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Two-stage classification methods for microarray data
Expert Systems with Applications: An International Journal
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
A Novel GA-Taguchi-Based Feature Selection Method
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Artificial Intelligence in 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
An effective refinement strategy for KNN text classifier
Expert Systems with Applications: An International Journal
A hybrid GA/SVM approach for gene selection and classification of microarray data
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
A novel forward gene selection algorithm for microarray data
Neurocomputing
| Hi-index | 12.05 |
The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and a small sample size. This makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. In this paper, correlation-based feature selection (CFS) and the Taguchi chaotic binary particle swarm optimization (TCBPSO) were combined into a hybrid method. The K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Experimental results show that this hybrid method effectively simplifies features selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.