DAGSVM vs. DAGKNN: An experimental case study with benthic macroinvertebrate dataset

Abstract

In this paper we examined the suitability of the Directed Acyclic Graph Support Vector Machine (DAGSVM) and Directed Acyclic Graph k-Nearest Neighbour (DAGKNN) method in classification of the benthic macroinvertebrate samples. We divided our 50 species dataset into five ten species groups according to their group sizes. We performed extensive experimental tests with every group, where DAGSVM was tested with seven kernel functions and DAGKNN with four measures. Feature selection was made by the scatter method [8]. Results showed that the quadratic and RBF kernel functions were the best ones and in the case of DAGKNN all measures produced quite similar results. Generally, the DAGSVM gained higher accuracies than DAGKNN, but still DAGKNN is a respectable option in benthic macroinvertebrate classification. © 2012 Springer-Verlag.