Texture classification using kernel-based techniques

Authors:
Carlos Fernandez-Lozano;Jose A. Seoane;Marcos Gestal;Tom R. Gaunt;Colin Campbell
Affiliations:
Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain;MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK;Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain;MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK;Department of Engineering Mathematics, University of Bristol, Bristol, UK
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

Citing 20
Cited 0

A note on genetic algorithms for large-scale feature selection

Pattern Recognition Letters
Texture analysis

Handbook of pattern recognition & computer vision
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
An introduction to variable and feature selection

The Journal of Machine Learning Research
Variable selection using svm based criteria

The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming

The Journal of Machine Learning Research
Using AUC and Accuracy in Evaluating Learning Algorithms

IEEE Transactions on Knowledge and Data Engineering
A statistical framework for genomic data fusion

Bioinformatics
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)

Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Large Scale Multiple Kernel Learning

The Journal of Machine Learning Research
MaZda A Software for Texture Analysis

ISITC '07 Proceedings of the 2007 International Symposium on Information Technology Convergence
A review of feature selection techniques in bioinformatics

Bioinformatics
An experimental comparison of performance measures for classification

Pattern Recognition Letters
Data Mining in Complex Diseases Using Evolutionary Computation

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Learning with Support Vector Machines

Learning with Support Vector Machines
Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection

Expert Systems with Applications: An International Journal
Texture analysis of poly-adenylated mRNA staining following global brain ischemia and reperfusion

Computer Methods and Programs in Biomedicine
Support vector machines for histogram-based image classification

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a high-dimensional textural heterogenous dataset is evaluated. This problem should be studied with specific techniques or a solution for decreasing dimensionality should be applied in order to improve the classification results. Thus, this problem is tackled by means of three differente techniques: an specific technique such as Multiple Kernel Learning, and two different feature selection techniques such as Support Vector Machines-Recursive Feature Elimination and a Genetic Algorithm-based approaches. We found that the best technique is Support Vector Machines-Recursive Feature Elimination, with a AUROC score of 92,45%.