Mapping microarray gene expression data into dissimilarity spaces for tumor classification
This paper presents a two-stage prediction model for microarray gene expression data. First, ReliefF is used to generate a subset with a number of top-ranked genes. Second, the samples with the subset of genes are mapped into a dissimilarity space. The classifier is able to separate the classes more easily than a feature-based model. Results show that the dissimilarity-based classifiers outperform the feature-based models.
gene expression dissimilarity space classification feature selection