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Rule inducted models for classifying water quality using diatoms as bio-indicators

机译：使用硅藻作为生物指示器对水质进行分类的规则电感模型

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In this paper we use the property of diatoms as bioindicators, to indentify which WQC is the taken sample using machine learning classifying algorithm - CN2. Important physical-chemical parameters such as conductivity, saturated oxygen and mostly used pH have defined range in which water class they belong. 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 where high coefficient of indication on certain process such as eutrophication, which means that they can be used as bio- indicators of water quality. The CN2 algorithm 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 we compare the classification accuracy 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-classification rule induction and compare these results to the single target.
机译：在本文中，我们使用硅藻作为生物indicer的性质，以识别使用机器学习分类算法的拍摄样品 - CN2。重要的物理化学参数，如电导率，饱和氧，主要是使用的pH定义了它们所属的水级的范围。这些物理化学参数对整个湖泊纤维网的影响有影响，从而扰乱了与它们之间的生物体的模式和相互作用，例如硅藻群体。这些社区具有高系数对某些过程的高度指示，例如富营养化，这意味着它们可以用作水质的生物指标。 CN2算法可以以形式产生规则，即适用于从硅藻丰度数据组织知识。在文献中，硅藻具有以相同方式组织的生态偏好。实验设置不仅可以满足算法属性，而且是硅藻群体的生态知识。我们使用了几种修改算法，从中比较了分类准确性或规则质量，以指向哪个实验被证明是最准确性和更一般的。本文中的一些规则与评估绩效一起呈现。基于CN2算法参数的修改，我们能够提取某些知识，这些知识表明，后来已经证明有效，或者在某些情况下是许多新发现的硅藻的新颖。在未来，我们计划调查CN2算法的更多修改，也可以实现多分类规则诱导并将这些结果与单个目标进行比较。

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