Survey on Efficient Feature Subset Selection Technique on High Dimensional Small Sized Data

Feature subset selection has the main attention of the research in the areas for which datasets possess high dimensional variables. During Classification, the high dimensional feature vectors of microarray data impose a high dimensional cost and the risk of over fitting. Hence there is a necessity to reduce the dimension with the help of feature selection. This survey paper considers Feature subset selection on classification for biomedical datasets with a less samples and large features or variables. Commonly, the performance of a classifier is degraded due to irrelevant features of high dimensional data. A conventional form of regularization gives majority class an equivalent or more emphasis, but here main focus is on Minority class so that overall Classifier�s performance can be improved.