Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106080
Title: A novel ensemble algorithm for tumor classification
Authors: Sun, Zhan-Li
Wang, Han
Lau, Wai-Shing
Seet Gim Lee, Gerald
Wang, Danwei
Lam, Kin-Man
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2013
Source: Sun, Z.-L., Wang, H., Lau, W.-S., Seet, G. L. G., Wang, D., & Lam, K.-M. (2013). A novel ensemble algorithm for tumor classification. 10th International Symposium on Neural Networks, 7952, 292-298.
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.
URI: https://hdl.handle.net/10356/106080
http://hdl.handle.net/10220/17966
DOI: 10.1007/978-3-642-39068-5_36
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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