TROPHIC DIATOM CLASSIFICATION USING NAÏVE BAYES - Naumoski, Andreja; Mitreski, Kosta (My University, 2013)
Knowledge discovery has been used in many different type of analysis and data types which lead to increased understanding of many natural processes and phenomena. This is why this process is important in the area of analysing environmental data. The topic and the goal of the paper is to used this process and the information contained in the measured data for given lake ecosystem and extract that information in an understandable form. This research aims to assess the relationships between the diatoms and the indicators of the environment using Naïve Bayes method learning technique. The diatoms are taken into account because they are ideal indicators of certain physical-chemical parameters and they can be classified into one of the trophic quality classes (TQCs). Before the algorithm processes the data, the input dataset is discretised. Then using the Naïve Bayes technique, several models for each TQC are obtained, presented and discussed. Then the obtained knowledge is verified with existing diatom ecological preference. Directions of future research and improvement for using this method for environmental data are given at the conclusion of the paper.