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.