Supervised Microalgae Classification in Imbalanced Dataset

Abstract

Microalgae are unicellular organisms that have physical characteristics such as size, shape or even the present structures. Classifying them manually may require great effort from experts since thousands of microalgae can be found in a small sample of water. Furthermore, the manual classification is not a trivial operation. The results show an important improvement in the classification quality when cost matrix and sampling methods are associated with supervised algorithm.