PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

DDAG K-TIPCAC: an ensemble method for protein subcellular localization
Alessandro Rozza, Gabriele Lombardi, Matteo Re, Elena Casiraghi and Giorgio Valentini
In: Supervised and Unsupervised Ensemble Methods and Their Applications(2010).


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 for protein subcellular location.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7219
Deposited By:Elena Casiraghi
Deposited On:10 March 2011