Please use this identifier to cite or link to this item:
Title: Microarray data classification using the Spectral-Feature-Based TLS Ensemble Algorithm
Authors: Sun, ZL
Wang, H
Lau, WS
Seet, G
Wang, DW
Lam, KM 
Keywords: Classifier combination
Fourier transform
Gabor filter
Microarray data classification
Sparse representation
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on nanobioscience, 2014, v. 13, no. 3, p. 289-299 How to cite?
Journal: IEEE transactions on nanobioscience 
Abstract: The reliable and accurate identification of cancer categories is crucial to a successful diagnosis and a proper treatment of the disease. In most existing work, samples of gene expression data are treated as one-dimensional signals, and are analyzed by means of some statistical signal processing techniques or intelligent computation algorithms. In this paper, from an image-processing viewpoint, a spectral-feature-based Tikhonov-regularized least-squares (TLS) ensemble algorithm is proposed for cancer classification using gene expression data. In the TLS model, a test sample is represented as a linear combination of the atoms of a dictionary. Two types of dictionaries, namely singular value decomposition (SVD)-based eigenassays and independent component analysis (ICA)-based eigenassays, are proposed for the TLS model, and both are extracted via a two-stage approach. The proposed algorithm is inspired by our finding that, among these eigenassays, the categories of some of the testing samples can be assigned correctly by using the TLS models formed from some of the spectral features, but not for those formed from the original samples only. In order to retain the positive characteristics of these spectral features in making correct category assignments, a strategy of classifier committee learning (CCL) is designed to combine the results obtained from the different spectral features. Experimental results on standard databases demonstrate the feasibility and effectiveness of the proposed method.
ISSN: 1536-1241
EISSN: 1558-2639
DOI: 10.1109/TNB.2014.2327804
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Jul 17, 2018

Page view(s)

Last Week
Last month
Citations as of Jul 15, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.