Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
Classification of microarray data with factor mixture models
Bioinformatics
An Epicurean learning approach to gene-expression data classification
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
A sequential feature extraction approach for naïve bayes classification of microarray data
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Gene selection and cancer microarray data classification via mixed-integer optimization
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Expert Systems with Applications: An International Journal
Partition-conditional ICA for Bayesian classification of microarray data
Expert Systems with Applications: An International Journal
Robust approach for estimating probabilities in Naïve-Bayes Classifier for gene expression data
Expert Systems with Applications: An International Journal
Gene selection and classification using Taguchi chaotic binary particle swarm optimization
Expert Systems with Applications: An International Journal
An efficient statistical feature selection approach for classification of gene expression data
Journal of Biomedical Informatics
Expert Systems with Applications: An International Journal
Mining microarray data to predict the histological grade of a breast cancer
Journal of Biomedical Informatics
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Gene expression data are a key factor for the success of medical diagnosis, and two-stage classification methods are therefore developed for processing microarray data. The first stage for this kind of classification methods is to select a pre-specified number of genes, which are likely to be the most relevant to the occurrence of a disease, and passes these genes to the second stage for classification. In this paper, we use four gene selection mechanisms and two classification tools to compose eight two-stage classification methods, and test these eight methods on eight microarray data sets for analyzing their performance. The first interesting finding is that the genes chosen by different categories of gene selection mechanisms are less than half in common but result in insignificantly different classification accuracies. A subset-gene-ranking mechanism can be beneficial in classification accuracy, but its computational effort is much heavier. Whether the classification tool employed at the second stage should be accompanied with a dimension reduction technique depends on the characteristics of a data set.