COMPUTATIONAL INTELLIGENCE APPROACHES FOR PROTEIN FOLD RECOGNITION

New approaches based on the implementation of support vector machine (SVM) and flexible neural trees (FNT) with the error correcting output codes (ECOC) are presented for recognition of multi-class protein folds. ECOC is used for reducing multi-class classification problem to multiple binary classification problems. The SVM and FNT trained by particle swarm optimization algorithm are then employed as the base classifier. The experimental results show that the proposed method can improve prediction accuracy by 4%-15% on two datasets containing 27 SCOP folds.