A novel ensemble algorithm for tumor classification

Authors:
Zhan-Li Sun;Han Wang;Wai-Shing Lau;Gerald Seet;Danwei Wang;Kin-Man Lam
Affiliations:
School of Electrical Engineering and Automation, Anhui University, China;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Mechanical and Systems Engineering, Newcastle University, United Kingdom;School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;Department of Electronic and Information Engineering, Hong Kong Polytechnic University, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

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Abstract

From the viewpoint of image processing, a spectral feature-based TLS (Tikhonov-regularized least-squares) ensemble algorithm is proposed for tumor classification using gene expression data. In the TLS model, a test sample is represented as a linear combination of atoms of an overcomplete dictionary. Two types of dictionaries, spectral feature-based eigenassays and spectral feature-based metasamples, are proposed for the TLS model. Experimental results on standard databases demonstrate the feasibility and effectiveness of the proposed method.