A novel feature selection approach based on constrained eigenvalues optimization

It is often tricky in real-life classification applications to select model features that would ensure an adequate sample classification, given a large number of candidate features. Our main contribution is threefold: (1) Evaluate the relevance and redundancy of feature. (2) Define the feature selection problem as eigenvalue computation problem with linear constraint. (3) Select the best features in an efficient way. We considered 20 UCI benchmark datasets to validate and test our approach. The results were compared with those obtained using one of the more widely used approaches, namely mRMR, the conventional features and two moderns state-of-the-art approaches. The experimental results revealed that our approach could improve the classification task, using only 20 % of the conventional features.