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Multi attribute decision making model using multi rough set: Case study classification of anger intensity of Javanese woman

机译:使用多粗糙集的多属性决策模型:爪哇妇女愤怒强度的案例研究分类

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Decision-making process typically involves multiple attributes. It is using a part or whole attributes to find the best decision from the alternatives. Some methods such as rough set are used to solve this problem but it has worse time complexity with respect to the numerous attributes. Hence, Multi Rough Set is proposed to improve the performance of rough set. In this study, this method used to classify the anger of Javanese woman's which require numerous attributes but has limited number of object. We divided the information table into several groups which has similarity attribute and it is computed simultaneously. The decision of each group as result of rough set and then used fuzzy rule set to obtain the final result. Using leave one out cross validation obtained 79% more accurate than using single rough set for all attribute.
机译:决策过程通常涉及多个属性。它使用部分或全部属性从替代方案中找到最佳决策。一些方法(例如粗糙集)用于解决此问题,但相对于众多属性,它的时间复杂度更差。因此,提出了多粗糙集以提高粗糙集的性能。在这项研究中,该方法用于对爪哇妇女的愤怒进行分类,这需要众多属性,但对象数量有限。我们将信息表分为具有相似属性的几组,并同时进行计算。将各组的决策作为粗糙集的结果,然后使用模糊规则集获得最终结果。对于所有属性,使用留一法交叉验证所获得的准确性比对单个粗糙集进行准确性高79%。

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