Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature selection in data mining
Data mining
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A review of feature selection techniques in bioinformatics
Bioinformatics
A Novel SVC Method Based on K-means
FGCN '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 03
Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Feature selection for genomic data sets through feature clustering
International Journal of Data Mining and Bioinformatics
LIBGS: A MATLAB software package for gene selection
International Journal of Data Mining and Bioinformatics
Hi-index | 0.00 |
With the development of genome research, finding method to classify cancer and detect biomarkers efficiently has become a challenging problem. In this paper, a novel multi-stage method for feature selection is proposed which considers all kinds of genes in the original gene set. The method eliminates the irrelevant, noisy and redundant genes and selects a subset of relevant genes at different stages. The proposed method is examined on microarray datasets of Leukemia, Prostate, Colon, Breast, Nervous and DLBCL by different classifiers and the best accuracies of the method in these datasets are 100%, 98.04%, 100%, 89.74%, 100% and 98.28%, respectively.