Subject: ecological modelling; biodiversity indices; diatoms; machine learning algorit
Year: 2020
Type: Article
Title: Evaluation of diatoms biodiversity models by applying different discretization on the class attribute
Author: Naumoski, Andreja
Author: Mircheva, Georgina
Author: Mitreski, Kosta
Abstract: 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 Naïve Bayes. The obtained models are also examined regarding resistance to over-fitting, as well as statistical significance.
Publisher: IEEE
Relation: Просторно-податочна анализа со ГИС за продавници и услуги поврзани со здравјето и спортот
Identifier: oai:repository.ukim.mk:20.500.12188/14694
Identifier: http://hdl.handle.net/20.500.12188/14694Identifier: 10.23919/mipro48935.2020.9245203
Identifier: http://xplorestaging.ieee.org/ielx7/9245088/9245075/09245203.pdf?arnumber=9245203
