A novel ensemble algorithm for tumor classification

• Main Authors: Sun, Zhan-Li, Wang, Han, Lau, Wai-Shing, Seet Gim Lee, Gerald, Wang, Danwei, Lam, Kin-Man
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/106080; http://hdl.handle.net/10220/17966; http://dx.doi.org/10.1007/978-3-642-39068-5_36
• Institution: Nanyang Technological University

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.