Supervised immune clonal evolutionary classification algorithm for high-dimensional data

Authors:
Ruochen Liu;Ping Zhang;Licheng Jiao;Yangyang Li
Affiliations:
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China
Venue:
Neurocomputing
Year:
2012

Citing 17
Cited 2

Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Regularized discriminant analysis for high dimensional, low sample size data

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards an artificial immune system for network intrusion detection: an investigation of dynamic clonal selection

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Uncorrelated linear discriminant analysis based on weighted pairwise Fisher criterion

Pattern Recognition
Feature Extraction and Uncorrelated Discriminant Analysis for High-Dimensional Data

IEEE Transactions on Knowledge and Data Engineering
A population-based artificial immune system for numerical optimization

Neurocomputing
An Expanded Lateral Interactive Clonal Selection Algorithm and Its Application

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Improved Clonal Selection Algorithm Combined with Ant Colony Optimization

IEICE - Transactions on Information and Systems
Fusion of clonal selection algorithm and differential evolution method in training cascade-correlation neural network

Neurocomputing
Gene transposon based clonal selection algorithm for clustering

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Rapid and brief communication: Uncorrelated heteroscedastic LDA based on the weighted pairwise Chernoff criterion

Pattern Recognition
A danger theory inspired artificial immune algorithm for on-line supervised two-class classification problem

Neurocomputing
A unified semi-supervised dimensionality reduction framework for manifold learning

Neurocomputing
Normalized dimensionality reduction using nonnegative matrix factorization

Neurocomputing
Learning and optimization using the clonal selection principle

IEEE Transactions on Evolutionary Computation
An improved LDA approach

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient and robust feature extraction by maximum margin criterion

IEEE Transactions on Neural Networks

Bi-objective feature selection for discriminant analysis in two-class classification

Knowledge-Based Systems
An analysis of unit tests of a flight software product line

Science of Computer Programming

Quantified Score

Hi-index	0.01

Visualization

Abstract

Classification is very difficult in high-dimensional spaces because learning methods suffer from the curse of dimensionality. In order to efficiently classify the high-dimensional data, a Supervised Immune Clonal Evolutionary Classification Algorithm (SICECA) is proposed in this paper. First, the automatic nonparametric Uncorrelated Discriminant Analysis (UDA) is adopted for Dimensionality Reduction (DR), which combines rank-preserving dimensionality reduction with constraint discriminant analysis so as to realize the extracted features statistically uncorrelated. Then, an Immune Clonal Evolutionary Algorithm (ICEA) based on clonal selection principle in immunology is proposed to act as classifier. In the experiments, first of all, 11 UCI data sets, four texture images and three Synthetic Aperture Radar (SAR) images are used to test the performance of SICECA. SICECA is also compared with three existing algorithms in terms of classification accuracy and running time. In addition, SICECA is applied to a real world application, namely, face recognition, with a good performance obtained.