Abstract: The most important subtype of non-Hodgkin-s
lymphoma is the Diffuse Large B-Cell Lymphoma. Approximately
40% of the patients suffering from it respond well to therapy,
whereas the remainder needs a more aggressive treatment, in order to
better their chances of survival. Data Mining techniques have helped
to identify the class of the lymphoma in an efficient manner. Despite
that, thousands of genes should be processed to obtain the results.
This paper presents a comparison of the use of various attribute
selection methods aiming to reduce the number of genes to be
searched, looking for a more effective procedure as a whole.
Abstract: Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents experimenters using the algorithm for hierarchical classification called Multi-label Hierarchical Classification using a Competitive Neural Network (MHC-CNN). It was tested in ten datasets the Gene Ontology (GO) Cellular Component Domain. The results are compared with the Clus-HMC and Clus-HSC using the hF-Measure.