Abstract
In this chapter we present an ensemble classifier that performs multi-class classification by combining several kernel classifiers through Decision Direct Acyclic Graph (DDAG). Each base classifier, called K-TIPCAC, is mainly based on the projection of the given points on the Fisher subspace, estimated on the training data, by means of a novel technique. The proposed multiclass classifier is applied to the task of protein subcellular location prediction, which is one of the most difficult multiclass prediction problems in modern computational biology. Although many methods have been proposed in the literature to solve this problem all the existing approaches are affected by some limitations, so that the problem is still open. Experimental results clearly indicate that the proposed technique, called DDAG K-TIPCAC, performs equally, if not better, than state of the art ensemble methods aimed at multi-class classification of highly unbalanced data.
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Rozza, A., Lombardi, G., Re, M., Casiraghi, E., Valentini, G., Campadelli, P. (2011). A Novel Ensemble Technique for Protein Subcellular Location Prediction. In: Okun, O., Valentini, G., Re, M. (eds) Ensembles in Machine Learning Applications. Studies in Computational Intelligence, vol 373. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22910-7_9
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