Directed acyclic graph support vector machines in classification of benthic macroinvertebrate samples

Abstract

Freshwater areas are an evanescent resource in the globe and, hence, the water quality assessment and examination of human induced changes on aquatic ecosystems are important. Benthic macroinvertebrates are excellent indicators of the state of freshwater area due to their intermediate life cycle and their ability to react changes in an aquatic ecosystem. However, identifying the benthic macroinvertebrates is a slow and expensive task when the process is human-made. Thus, we have an excellent opportunity to save resources by using sophisticated machine learning and pattern recognition methods. In this research we applied Directed Acyclic Graph Support Vector Machine (DAGSVM) and six other baseline methods in automated benthic macroinvertebrate classification. We performed wide experimental tests with six different feature sets where one was chosen by Scatter method. Furthermore, in the case of DAGSVM we tested every feature set with seven kernel functions. Experimental results showed that Scatter method is a good feature selection algorithm and we obtained above 80 % accuracy in classifications which is a very good result for such a challenging classification task. From all tested classification methods DAGSVM showed to be the best alternative for this classification task.