Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multiple order gradient feature for macro-invertebrate identification using support vector machines
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Classification and retrieval on macroinvertebrate image databases
Computers in Biology and Medicine
Comparing the one-vs-one and one-vs-all methods in benthic macroinvertebrate image classification
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Comparison of multiclass SVM decomposition schemes for visual object recognition
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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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.