ACQUISITION OF KANSEI DECISION RULES OF COFFEE FLAVOR USING ROUGH SET METHOD
ACQUISITION OF KANSEI DECISION RULES OF COFFEE FLAVOR USING ROUGH SET METHOD
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DOI：
10.5057/kei.5.4_41
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
T. Nishino;M. Nagamachi;M. Sakawa
中科院分区：
文献类型：
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作者：
T. Nishino;M. Nagamachi;M. Sakawa
在产品的设计元素和人类评价之间获取有效的决策规则是很重要的，但在我们必须处理线性不可分割和大量模糊数据的情况下，这一点尤为困难。我们提出了粗糙集方法来处理这些情况。在本文中，我们还提出了利用增益图来可视化产品属性对决策的影响与评估事件数量之间的关系。利用增益图，我们可以根据每个评价词的增益特性建立一个合适的参数。我们展示了我们提出的粗糙集方法在咖啡风味评价（即味道和香气）与咖啡制造条件模式之间的决策规则提取中的应用。结果表明，粗糙集方法能够更有效地从感官和感觉等模糊数据中提取制造条件的组合规则。我们还发现，用ƒÀ-upper近似和ƒÀ-lower近似可以很容易地得到更一般的规则。
The acquisition of effective decision rules between design elements of products and human evaluations is significant , but difficult especially in the cases where we have to handle linearly inseparable and much ambiguous data. We have proposed the rough set method to handle these cases. In this paper, we also propose the utilization of gain chart that can visualize a relation between the effects of product attributes on decision and the number of evaluation events . Using the gain chart, we can set up a suitable parameter corresponding with gain property of each evaluation word. We show an application of our proposed rough set method to the extraction of decision rules between coffee flavor evaluations , i.e., taste and aroma, and the pattern of coffee manufacturing conditions. The results showed that our rough set method enabled more effectively to extract the combination rules of manufacturing conditions from much ambiguous data like sense and feeling. We also found out that it is easy to obtain more general rules by ƒÀ-upper approximation as well as ƒÀ-lower approximation.