A feature_core and SVM-based algorithm for identification of bioprocess-specific genome features

This work presents a SVM and feature_core-based algorithm for identification of key genome features. The significant difficulty in selecting key features from a high dimensional space of features is the curse of dimensions in searching. For this reason, a feature_core-based search strategy is proposed in this algorithm. The strategy integrates the forward selection and backward elimination techniques. In this algorithm all key genome features are formed through the agglomeration and expansion of the feature core based on potential information about relevance in a SVM classifier. The application given proves that the algorithm is faster and more efficient than other methods such as clustering, and the single SVM-based method.