Half-Against-Half Structure with SVM and k-NN Classifiers in Benthic Macroinvertebrate Image Classification

We investigated how Half-Against-Half Support Vector Machine (HAH-SVM) and Half-Against-Half kNearest Neighbour (HAH-KNN) methods succeed in the classification of the benthic macroinvertebrate images. Automated taxa identification of benthic macroinvertebrates is a slightly researched area and in this paper HAH-KNN was for the first time applied to this application area. The main problem, when Half-Against-Half structure is used, is to find the right way to divide the classes in nodes. This problem was solved by using two different approaches. Firstly, we applied the Scatter method for the class division problem. Secondly, we formed the class divisions in a HalfAgainst-Half structure by a random choice. We performed extensive experimental tests with four different feature sets and tested every feature set with seven different kernel functions in the case of HAH-SVM. Furthermore, HAHKNN was tested with four measures. The tests showed that by the Scatter method and random choice formed HAH-SVMs performed the classification problem very well obtaining over 95% accuracy while with HAH-KNN above 92% accuracy was achieved. Moreover, the 7D and 15D feature sets together with the RBF kernel function are good choices for this classification task when HAH-SVM was used and 15D feature set, when HAH-KNN was used. Generally speaking, Half-Against-Half structure is a promising multiclass extension for SVM and an interesting variant for k-NN classifier.