Diatom Classification with Novel Bell Based Classification Algorithm

Diatoms are ideal indicators of certain physical-chemical parameters and in the relevant literature they are classified into one of the water quality classes (WQCs). Using information technologies methods, we can classify old and new diatoms directly from measured data. In this direction, a novel method for diatom classification is proposed in this paper. The classification models are induced by using modified bell fuzzy membership functions (MFs) in order to make more accurate models. An intensive comparison study of the fuzzy MFs distribution with the proposed method and the classical classification algorithms on the classification accuracy is studied. Based on this evaluation results, three models are presented and discussed. The experimental results have shown that the proposed algorithm remains interpretable, robust on data change and achieve highest classification accuracy. The obtain results from the classification models are verified with existing diatom ecological preference and for some diatoms new knowledge is added.