A comparative analysis of feature selection algorithms on classification of gene microarray dataset

Analysis of gene expression is important in many fields of biological research in order to retrieve the required information. As the time advances, the illness in general and cancer in particular have become more and more complex and complicated, in detecting, analyzing and curing. Cancer research is one of the major research areas in the medical field. Accurate prediction of different tumor types has great value in providing better treatment and toxicity minimization on the patients. To minimize it, the data mining algorithms are important tool and the most extensively used approach to classify gene expression data and plays an important role for cancer classification. One of the major challenges is to discover how to extract useful information from datasets. This research is based on recent advances in the machine learning based microarray gene expression data analysis with three feature selection algorithms.