Comparative study on supervised learning methods for identifying phytoplankton species
This addresses the challenge of monitoring environmental and climate changes through phytoplankton identification, but it is incremental as it compares existing methods with a new feature type.
The paper tackled the problem of identifying phytoplankton species by building a framework to compare different feature types and classifiers, and found that Random Forest with proposed features achieved an average accuracy of 98.24%.
Phytoplankton plays an important role in marine ecosystem. It is defined as a biological factor to assess marine quality. The identification of phytoplankton species has a high potential for monitoring environmental, climate changes and for evaluating water quality. However, phytoplankton species identification is not an easy task owing to their variability and ambiguity due to thousands of micro and pico-plankton species. Therefore, the aim of this paper is to build a framework for identifying phytoplankton species and to perform a comparison on different features types and classifiers. We propose a new features type extracted from raw signals of phytoplankton species. We then analyze the performance of various classifiers on the proposed features type as well as two other features types for finding the robust one. Through experiments, it is found that Random Forest using the proposed features gives the best classification results with average accuracy up to 98.24%.