Dr.r Mallika

Linear Data Mining Model with Very Few Minimum Gene Features: Cancer Data Classification Dr.r Mallika

Microarray classification has been a hot topic in recent years and attracted the attention of many researchers from different research fields such as data mining, machine learning and statistics. Gene expression data analysis plays a vital role in medical diagnosis and drug discovery. With huge volume of gene expression data,the possibilities of cancer classification have to be explored. Many methods have been proposed with promising results. Various statistical gene selection techniques, which are an integral pre-processing step for classification along with few supervised classification methods were used in various works. The initiation of efficient classification algorithm for cancer gene expression data has been exploded in health sector during recent years. Particular application of Data mining algorithms for microarray technologies is in cancer research with a goal of early diagnosis of cancer. In machine learning community,supervised learning is to build predictive models using gene expression measurements of a number of individuals with known class membership. This research work presents a new and novel supervised classification method for cancer classification and prediction.