Pubblicazione scientifica

Comparative study on supervised learning methods for identifying phytoplankton species

FILONE TECNOLOGICO	TEMA
produzione e raccolta

RIASSUNTO

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%.

DATA

Data di pubblicazione: 01/07/2016

AUTORI

THI-THU-HONG PHAN
EMILIE POISSON CAILLAULT
ANDRE BIGAND

ENTE DI AFFERENZA

DEPT COMPUTER SCIENCE VIETNAM NATL UNIV AGRICULTURE HANOI VIETNAM
LISIC UNIV LITTORAL COTE D OPALE F 62228 CALAIS FRANCE

RIVISTA

2016 IEEE Sixth International Conference on Communications and Electronics (ICCE) (Page(s): 283-288)

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