Examining the Classification Accuracy of TSVMs with ?Feature Selection in Comparison with the GLAD Algorithm

TL;DR

This study compares the classification accuracy of TSVMs with feature selection to the GLAD algorithm on gene expression data, demonstrating TSVM-RFE's superior performance in semi-supervised microarray data classification.

Contribution

It introduces a comparison between TSVM-RFE and GLAD algorithms, highlighting the effectiveness of TSVMs with feature selection in gene expression classification.

Findings

TSVM-RFE outperforms SVM with RFE and GLAD in accuracy

Feature selection improves classification performance

Semi-supervised TSVMs are effective for gene expression data

Abstract

Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are insufficient using Support Vector Machines (SVM) algorithms. Therefore, in this paper, we suggest Transductive Support Vector Machines (TSVMs) as semi-supervised learning algorithms, learning with both labelled samples data and unlabelled samples to perform the classification of microarray data. To prune the superfluous genes and samples we used a feature selection method called Recursive Feature Elimination (RFE), which is supposed to enhance the output of classification and avoid the local optimization problem. We examined the classification prediction accuracy of the TSVM-RFE algorithm in comparison with the Genetic Learning Across Datasets (GLAD) algorithm, as both are semi-supervised learning methods. Comparing these two methods, we found that the TSVM-RFE surpassed both a SVM using RFE and GLAD.