Evaluation of diatoms biodiversity models by applying different discretization on the class attribute
Aleksandar Naumoski, Georgina Mirceva, Kosta Mitreski · 2020
One of the main goals of knowledge discovery from environmental data is through data analysis to find the relationship between the living organisms, represented with the diversity of the diatoms community members, and the characteristics of the environment. This is very important information for both ecologists and decision makers. Therefore, in this paper we apply various machine learning algorithms for revealing this relationship by using different number of discretization levels for the target attribute. The target attribute represents the biodiversity index of the community and it is calculated based on the abundances of the diatoms. For building models, different types of machine learning algorithms are considered including decision trees, rule induction algorithms, neural networks and Naive Bayes. The obtained models are also examined regarding resistance to over-fitting, as well as statistical significance.