Boosting support vector machines using multiple dissimilarities

Authors:
Ángela Blanco;Manuel Martín-Merino
Affiliations:
Universidad Pontificia de Salamanca, Salamanca, Spain;Universidad Pontificia de Salamanca, Salamanca, Spain
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Year:
2007

Citing 13
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Bagging predictors

Machine Learning
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Re-designing distance functions and distance-based applications for high dimensional data

ACM SIGMOD Record
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
What Is the Nearest Neighbor in High Dimensional Spaces?

VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A generalized kernel approach to dissimilarity-based classification

The Journal of Machine Learning Research
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods

The Journal of Machine Learning Research
A New Sammon Algorithm for Sparse Data Visualization

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Cluster Analysis for Gene Expression Data: A Survey

IEEE Transactions on Knowledge and Data Engineering
Prediction error estimation: a comparison of resampling methods

Bioinformatics
Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)

Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support Vector Machines (SVM) are powerful machine learning techniques that are able to deal with high dimensional and noisy data. They have been successfully applied to a wide range of problems and particularly to the analysis of gene expression data. However SVM algorithms rely usually on the use of the Euclidean distance that often fails to reflect the object proximities. Several versions of the SVM have been proposed that incorporate non Euclidean dissimilarities. Nevertheless, different dissimilarities reflect complementary features of the data and no one can be considered superior to the others. In this paper, we present an ensemble of SVM classifiers that reduces the misclassification error combining different dissimilarities. The method proposed has been applied to identify cancerous tissues using Microarray gene expression data with remarkable results.