Combining single class features for improving performance of a two stage classifier

We propose a feature selection–based approach for improving classification performance of a two stage classification system in contexts where a high number of features is involved. A problem with a set of N classes is subdivided into a set of N two class problems. In each problem, a GA–based feature selection algorithm is used for finding the best subset of features. These subsets are then used for training N classifiers. In the classification phase, unknown samples are given in input to each of the trained classifiers by using the corresponding subspace. In case of conflicting responses, the sample is sent to a suitably trained supplementary classifier. The proposed approach has been tested on a real world dataset containing hyper–spectral image data. The results favourably compare with those obtained by other methods on the same data.