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Fuzzy decision trees in the support of breastfeeding

机译:母乳喂养支持中的模糊决策树

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Decision trees are relative known and often used intelligent tool for decision support. They are convenient for capturing the knowledge from vast amount of data. The result or decision model is represented in hierarchical manner, where the significance or contribution of single attribute to final decision is shown very clearly. when the decision tree is built upon real-world data this data is more often numeric than discrete. While it is in human nature to better use the words than numbers in order to describe something the fuzzy logic theory was introduced to fill this gap. Description of the attribute properties using fuzzy logic approach is represented by vector of plausibility where the coordinates between 0 and 1 show the plausibility of attribute to its subsets of possible attribute values. This is the way to successfully overcome the problem of boundary values between attribute subsets where the sharp determined boundaries can incorrectly affect the final result a lot. In Laboratory for system design we have developed the software tool for building the decision trees with fuzzy heuristics function. The tool was used upon the data collected from breastfeeding booklet and the results are used for developing different advisory systems for health-care professionals as well as for breastfeeding support groups and mothers who have access to Internet.
机译:决策树是相对知名的,通常使用用于决策支持的智能工具。它们方便从大量数据捕获知识。结果或决策模型以分层方式表示,其中单个属性对最终决定的重要性或贡献非常清楚地示出。当决策树基于真实数据时,此数据更频繁数字而不是离散的数字。虽然它在人性中更好地使用而不是数字来描述模糊逻辑理论被引入的东西以填补这种差距。使用模糊逻辑方法的属性属性由拼凑的向量表示,其中0到1之间的坐标显示属性与可能属性值的子集的合理性。这是成功克服属性子集之间的边界值问题的方法,其中急定确定的边界可能错误地影响最终结果。在系统设计实验室中,我们开发了用模糊启发式功能构建决策树的软件工具。该工具用于从母乳喂养小册子收集的数据,结果用于制定用于医疗保健专业人员的不同咨询系统,以及母乳喂养支持团体和可访问互联网的母亲。

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