Gene selection and PSO-BP classifier encoding a prior information

Authors:
Yu Cui;Fei Han;Shiguang Ju
Affiliations:
School of Computer Science and Telecommunication Engineering, Jiangsu University, Jiangsu, China;School of Computer Science and Telecommunication Engineering, Jiangsu University, Jiangsu, China;School of Computer Science and Telecommunication Engineering, Jiangsu University, Jiangsu, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2010

Citing 10
Cited 1

Modified constrained learning algorithms incorporating additional functional constraints into neural networks

Information Sciences: an International Journal
Constructing the gene regulation-level representation of microarray data for cancer classification

Journal of Biomedical Informatics
Logistic regression for disease classification using microarray data

Bioinformatics
A new constrained learning algorithm for function approximation by encoding a priori information into feedforward neural networks

Neural Computing and Applications
Support vector machines combined with feature selection for breast cancer diagnosis

Expert Systems with Applications: An International Journal
A New Approach Encoding a Priori Information for Function Approximation

CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
An efficient gene selection algorithm based on mutual information

Neurocomputing
A neural network-based biomarker association information extraction approach for cancer classification

Journal of Biomedical Informatics
Feature subset selection in large dimensionality domains

Pattern Recognition
Ensemble gene selection by grouping for microarray data classification

Journal of Biomedical Informatics

Particle swarm classification: A survey and positioning

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Selecting a relevant and discriminative combination of genes for cancer classification and building high-performing classifier are common and critical tasks in cancer classification problems. In this paper, a new approach is proposed to address the two issues at the same time. In details, BP neural network is employed to construct a classifier, and PSO algorithm is used to select a discriminative combination of genes and optimize the BP classifier accordingly. Besides, sample's prior information is encoded into PSO algorithm for better performance. The proposed approach is validated on the leukemia data set. The experimental results show that our novel method selects fewer discriminative genes while has comparable performance to the traditional classification approaches.