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Minimum Attribute number in Decision Table Based on Maximum Entropy Principle

机译:基于最大熵原理的决策表中的最小属性编号

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Decision tables are always extremely important objects in data mining. People often require the more simple decision table in order to reduce the scale of tables. But a decision table is not always the most simple, so we have to try reducting it to learn which condition attributes are essential. It is known that the reduct results are not usually unique and the cardinal numbers of condition attributes set in different deducted tables of the same tables are different From research findings on redacted tables, however, we can find out a simplest condition attributes set and call it Minimum Attribute Set According to information theory, in this paper, we have deduced a formula to calculate the cardinal number of the Minimum Attribute Set, which is called Minimum Attribute Number. Moreover, before reducted we can just know whether the table is the simplest one or not. Eventually, we give a simple test example.
机译:决策表始终是数据挖掘中的极其重要的对象。人们经常需要更简单的决策表以减少表格的规模。但是,决策表并不总是最简单的,所以我们必须尝试将其减少来了解哪些条件属性是必不可少的。众所周知,减效结果通常不是唯一的,并且在相同表的不同扣除表中设置的条件属性的基数属性与冗余表的研究结果不同,但是,我们可以找到一个最简单的条件属性,并调用它根据信息理论设置的最小属性集,在本文中,我们推导了一个公式来计算最小属性集的基数,该条件称为最小属性编号。而且,在还原之前,我们只能知道表是否是最简单的表。最终,我们提供一个简单的测试示例。

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