Open Access. Powered by Scholars. Published by Universities.®

Genetics and Genomics Commons

Open Access. Powered by Scholars. Published by Universities.®

The University of Southern Mississippi

Feature selection

Articles 1 - 2 of 2

Full-Text Articles in Genetics and Genomics

A Comparative Study Of Different Machine Learning Methods On Microarray Gene Expression Data, Mehdi Pirooznia, Jack Y. Yang, Mary Qu Yang, Youping Deng Jan 2007

A Comparative Study Of Different Machine Learning Methods On Microarray Gene Expression Data, Mehdi Pirooznia, Jack Y. Yang, Mary Qu Yang, Youping Deng

Faculty Publications

Background

Several classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. The accuracy of these methods has been calculated with validation methods such as v-fold validation. However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results.

Results

In this study, we compared the efficiency of the classification methods including; SVM, RBF Neural Nets, …


Improving Prediction Accuracy Of Tumor Classification By Reusing Genes Discarded During Gene Selection, Jack Y. Yang, Guo-Zheng Li, Hao-Hua Meng, Mary Qu Yang, Youping Deng Jan 2007

Improving Prediction Accuracy Of Tumor Classification By Reusing Genes Discarded During Gene Selection, Jack Y. Yang, Guo-Zheng Li, Hao-Hua Meng, Mary Qu Yang, Youping Deng

Faculty Publications

Background

Since the high dimensionality of gene expression microarray data sets degrades the generalization performance of classifiers, feature selection, which selects relevant features and discards irrelevant and redundant features, has been widely used in the bioinformatics field. Multi-task learning is a novel technique to improve prediction accuracy of tumor classification by using information contained in such discarded redundant features, but which features should be discarded or used as input or output remains an open issue.

Results

We demonstrate a framework for automatically selecting features to be input, output, and discarded by using a genetic algorithm, and propose two algorithms: GA-MTL …