A Novel Ensemble Technique for Protein Subcellular Location Prediction
Protein subcellular location prediction is one of the most difficult multiclass prediction problems in modern computational biology. Many methods have been proposed in the literature to solve this problem but all the existing approaches are affected by some limitations. In this contribution we propose a novel method for protein subcellular location prediction that performs multiclass classification by combining kernel classifiers through DDAG. Each base classifier, called K-TIPCAC, projects the points on a Fisher subspace estimated on the training data by means of a novel technique. Experimental results clearly indicated that DDAG K-TIPCAC performs equally, if not better, than state-of-the-art ensemble methods in multiclass unbalanced problems.