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Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions

机译:结构识别离散化算法用于贝叶斯网络的水质预测

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Bayesian networks have become a popular modelling technique in many fields, however there are several design decisions that, if poorly made, can result in models with insufficient evidence to make good predictions. One such decision is how to discretise the continuous nodes. The lack of a commonly accepted algorithm for achieving this makes it a difficult task for novice data modellers. We present a structure aware discretisation algorithm that minimises the number of missing values in the conditional probability tables by taking into account the network structure. It also prevents users from having to specify the exact number of bins. Results from two water quality case studies in south-east Queensland showed that the algorithm has potential to improve the discretisation process over equal case discretisation and demonstrates the suitability of Bayesian networks for this field.
机译:贝叶斯网络已成为许多领域中流行的建模技术,但是有一些设计决策,如果做得不好,可能导致模型缺乏足够的证据来做出良好的预测。这样的决定之一就是如何离散化连续节点。缺乏普遍接受的算法来实现这一点,这对新手数据建模者来说是一项艰巨的任务。我们提出了一种结构感知的离散化算法,该算法通过考虑网络结构来最大限度地减少条件概率表中缺失值的数量。它还可以防止用户必须指定箱的确切数量。来自昆士兰州东南部的两个水质案例研究的结果表明,与同等案例离散化相比,该算法具有改善离散化过程的潜力,并证明了贝叶斯网络对该领域的适用性。

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