期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2013
卷号：53
期号：1
出版社：Journal of Theoretical and Applied
摘要：With the rapid development of computer and information technology, an enormous amount of data in science and engineering had been generated in massive scale. Also, the diversity of data, the data mining tasks and approaches pose many challenges to research in data mining. The data mining field has widespread applications including advance diagnosis, market analysis, business management and decision support. But in medical field, illness in common and cancer in particular have become more and more complex and complicated one. To solve the problem in data mining, knowledge discovery tools had been used mainly in research environment. The data mining algorithms are important tool and the most extensively used approach to classify gene expression data and play an important role for classification. Classification is a data mining task and is an effective method to classify the data in the process of knowledge discovery .One of the major challenges faced by many scientists today is the analysis of the explosion of microarray gene expression data. This research is based on machine learning particularly microarray gene expression data analysis . In this paper, the authors have analyzed and compared the two statistical based feature selection algorithms namely Chi Square and T � Test Methods.
关键词：Feature Selection; Gene; Microarray; Data Mining; Machine Learning
