JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Design of an Optimized Classifier based on Feature Elimination for Gene Selection
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Design of an Optimized Classifier based on Feature Elimination for Gene Selection
Lee, Byung-Kwan; Park, Seok-Gyu; Tifani, Yusrina;
  PDF(new window)
 Abstract
This paper proposes an optimized classifier based on feature elimination (OCFE) for gene selection with combining two feature elimination methods, ReliefF and SVM-RFE. ReliefF algorithm is filter feature selection which rank the data by the importance of the data. SVM-RFE algorithm is a wrapper feature selection which wrapped the data and rank the data based on the weight of feature. With combining these two methods we get less error rate average, 0.3016138 for OCFE and 0.3096779 for SVM-RFE. The proposed method also get better accuracy with 70% for OCFE and 69% for SVM-RFE.
 Keywords
Feature elimination method;OCFE;ReliefF;SVM-REF;
 Language
English
 Cited by
 References
1.
V. B. Canedo, N.S. Marono, A.A. Betanoz, Distributed feature selection: An application to microarray data classification, Applied soft computing, vol.30, pp.136-150, May 2015. crossref(new window)

2.
X. Zhou, J. Wang, Feature selection for image classification based on a new ranking criterion, Journal of Computer and Communications, vol.3, pp. 74-79. March 2015.

3.
I. Guyon, J. Wetson, S. Barnhill, M. D. and V. Vapnik, Gene Selection for Cancer Classification using Support Vector Machines, Machine Learning, vol. 46, pp.389-422, 2002 crossref(new window)

4.
Y. Guerbai, Y. Chibani, B. Hadjadji, The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters, Pattern Recognition, vol. 48, no.1, pp.103-113, January 2015. crossref(new window)

5.
K.S. Shin, T.S. Lee, H. J. Kim, An application of support vector machines in bankruptcy prediction model, Expert Systems with Applications vol.28, no.1, pp.127-135, January 2005. crossref(new window)

6.
O. Devos, G. Downey, L. Dupochel, Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils, Food Chemistry, vol. 148, pp.124-130, April 2014. crossref(new window)

7.
M.R. Sikonja, I. Kononenko, Theoritical and Empirical Analysis of ReliefF and RReliefF, Machine Learning, vol.53, no.1, pp.23-69, October 2003. crossref(new window)

8.
I. Kononenko, M.R. Sikonja, U. Pompe, ReliefF for estimation and discretization of attributes in classification, regression, and ILP problems, pp.1-15, 1996

9.
J. Weston, A. Eliseff, G. BakIr, F. Sinz, The Spider. Available: http://people.kyb.tuebingen.mpg.de/spider/main.html

10.
Alon et al, Package 'ColonCA'. http://microarray.princeton.edu/oncology/affydata/index.htmle