Abstract
In this paper we use the property of diatoms as bioindicators, to indentify which physical-chemical parameters are contained in the taken sample using machine learning algorithm – CN2. Important physical-chemical parameters such as conductivity, saturated oxygen, pH, organic chemical parameters and metals are important in the process of environmental monitoring. These physical-chemical parameters have influence on the entire lake web food chain, thus disturbing the organism’s patterns and interactions between them, such as diatoms community. These communities have high coefficient of indication on certain process such as eutrophication and presence or absence of certain physical-chemical parameters, which means that they can be used as bio-indicators of water quality. The machine learning algorithm – CN2 can produce rules in a form IF-THEN which is suitable for organizing knowledge from diatoms abundance data. In literature the diatoms have ecological preference organized in the same manner. The experimental setup is build to satisfy not only the algorithm properties, but also the ecological knowledge of the diatoms community. We used several modifications of the algorithm, from which then we compare the compactness and coverage of the induced rule. Nevertheless, for regression problems we compare the correlation coefficient, root mean square error (RMSE) and relative root mean square error (RRMSE) or rule quality to point which experiment proved to be most accuracy and more general. Several of the rules are presented in this paper together with the evaluation performance.
Based on modifications of the CN2 algorithm parameters, we were able to extract certain knowledge form the data, which later have proved to be valid, or in some cases is novel for many newly discovered diatoms. In future we plan to investigate more modifications of the CN2 algorithm, also to implement multi-target rule induction and compare these results to the single target.
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Naumoski, A., Mitreski, K. (2010). Rule Induction of Physical-Chemical Water Property from Diatoms Community. In: Davcev, D., Gómez, J.M. (eds) ICT Innovations 2009. ICT Innovations 2009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10781-8_22
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DOI: https://doi.org/10.1007/978-3-642-10781-8_22
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