Feature selection and dimensionality reduction on gene expressions

Breast cancer is the most common type of cancer among women. Early diagnosis of the breast cancer plays an important role in treating the disease. Thousands of genes microarray data is often used in cancer diagnosis. However, many of these genes which are used in the diagnosis of disease do not have a meaningful pattern. Also, to classify thousands of genes are not good in terms of performance. Therefore, it is very important to make a correct diagnosis with a small number of genes. In this study, Fisher correlation score and T test were firstly applied for gene selection. After filtering, three different approaches were applied. The first method is feature generation and dimensionality reduction with principal component analysis. The second method is feature generation and feature selection with discrete cosine transform. The third method is feature selection with filtering data.