A Hybrid Both Filter and Wrapper Feature Selection Method for Microarray Classification
This incremental improvement addresses feature selection for disease analysis and cancer diagnosis using microarray data.
The paper tackled the problem of high-dimensional gene expression data in microarray classification by proposing a hybrid feature selection method combining information gain and improved binary particle swarm optimization, resulting in fewer selected gene subsets and better classification accuracy.
Gene expression data is widely used in disease analysis and cancer diagnosis. However, since gene expression data could contain thousands of genes simultaneously, successful microarray classification is rather difficult. Feature selection is an important pre-treatment for any classification process. Selecting a useful gene subset as a classifier not only decreases the computational time and cost, but also increases classification accuracy. In this study, we applied the information gain method as a filter approach, and an improved binary particle swarm optimization as a wrapper approach to implement feature selection; selected gene subsets were used to evaluate the performance of classification. Experimental results show that by employing the proposed method fewer gene subsets needed to be selected and better classification accuracy could be obtained.