  Feature selection is frequently used as a pre-processing step to machine
learning. It is a process of choosing a subset of original features so that the
feature space is optimally reduced according to a certain evaluation criterion.
The central objective of this paper is to reduce the dimension of the data by
finding a small set of important features which can give good classification
performance. We have applied filter and wrapper approach with different
classifiers QDA and LDA respectively. A widely-used filter method is used for
bioinformatics data i.e. a univariate criterion separately on each feature,
assuming that there is no interaction between features and then applied
Sequential Feature Selection method. Experimental results show that filter
approach gives better performance in respect of Misclassification Error Rate.
