The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Matrix computations (3rd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
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
Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)
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DNA Microarray technology allow us to identify cancerous tissues considering the gene expression levels across a collection of related samples. Several classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) have been applied to this problem. However, they are usually based on Euclidean distances that fail to reflect accurately the sample proximities. Several classifiers have been extended to work with non-Euclidean dissimilarities although none outperforms the others because they misclassify a different set of patterns. In this paper, we combine different kind of dissimilarity based classifiers to reduce the misclassification errors. The diversity among classifiers is induced considering a set of complementary dissimilarities for three different type of models. The experimental results suggest that the algorithm proposed helps to improve classifiers based on a single dissimilarity and a widely used combination strategy such as Bagging.