Data analytics: feature extraction for application with small sample in classification algorithms

This paper focuses on improving the classification accuracy for supervised learning in areas of application with very few training data and with extremely available high dimensionality. This paper proposes a framework which acts as a decision support system incorporating both feature selection and feature extraction to improvise the classification accuracy. The feature selection technique comprises redundancy elimination and relevance analysis. Feature subset selection problems eliminate features which are redundant by using correlation-based maximum spanning tree. But, the eliminated features may contain useful information which may contribute in determining the target or class labels. The principal components are extracted from the eliminated features and they are complemented with the selected features to perform classification. The superiority of the proposed method over other feature selection methods, in terms of computational complexity and classification accuracy, is established extensively on various datasets.