Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22368
Title: Feature selection for self-supervised classification with applications to microarray and sequence data
Authors: Kung, SY
Mak, MW 
Keywords: Doubly supervised
Feature selection
Pairwise approach
Self-supervised
Subcellular localization
SVM
Symmetric doubly supervised
Vectorization
Issue Date: 2008
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Source: IEEE journal on selected topics in signal processing, 2008, v. 2, no. 3, p. 297-309 How to cite?
Journal: IEEE Journal on Selected Topics in Signal Processing 
Abstract: Learning strategies are traditionally divided into two categories: unsupervised learning and supervised learning. In contrast, for feature selection, there are four different categories of training scenarios: 1) unsupervised; 2) (regular) supervised; 3) self-supervised (SS); and 4) doubly supervised. Many genomic applications naturally arise in either (regular) supervised or self-supervised formulation. The distinction of these two supervised scenarios lies in whether the class labels are assigned to the samples versus the features. This paper explains how to convert an SS formulation into a symmetric doubly supervised (SDS) formulation by a pairwise approach. The SDS formulation offers more explicit information for effective feature selection than the SS formulation. To harness this information, the paper adopts a selection scheme called vector-index-adaptive SVM (VIA-SVM), which is based on the fact that the support vectors can be subdivided into different groups each offering quite distinct prediction performance. Simulation studies validate that VIA-SVM performs very well for time-course microarray data. This paper further proposes a fusion strategy to integrate the diversified information embedded in the SDS formulation. Simulation studies on protein sequence data for subcelluar localization confirm that the prediction can be significantly improved by combining VIA-SVM with relevance scores (e.g., SNR) and redundancy metrics (e.g., Euclidean distance).
URI: http://hdl.handle.net/10397/22368
DOI: 10.1109/JSTSP.2008.923843
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

5
Last Week
0
Last month
1
Citations as of Aug 18, 2017

WEB OF SCIENCETM
Citations

3
Last Week
0
Last month
Citations as of Aug 12, 2017

Page view(s)

23
Last Week
1
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



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