A Novel Dimensionality Reduction Approach to Improve Microarray Data Classification

Abstract

Cancer tumor prediction and diagnosis at an early stage has become a necessity in cancer research, as it provides an increase in the treatment success chances. Recently, DNA microarray technology became a powerful tool for cancer identification, that can analyze the expression level of a different and huge number of genes simultaneously. In microarray data, the large genes number versus a few records may affect the prediction performance. In order to handle this "curse of dimensionality” constraint of microarray dataset while improving the cancer identification performance, a dimensional reduction phase is necessary. In this paper, we proposed a framework that combines dimensional reduction methods and machine learning algorithms in order to achieve the best cancer prediction performance using different microarray datasets. In the dimensional reduction phase, a combination of feature selection and feature extraction techniques was proposed. Pearson and Ant Colony Optimization was used to select the most important genes. Principal Component Analysis and Kernel Principal Component Analysis were used to linearly and non-linearly transform the selected genes to a new reduced space. In the cancer identification phase, we proposed four algorithms C5.0, Logistic Regression, Artificial Neural Network, and Support Vector Machine. Experimental results demonstrated that the framework performs effectively and competitively compared to state-of-the-art methods.