如果属性域是有限 的,那么则称该属性为分类属性(categoricalattribute)或离散属性(discrete attribute)。这些属性中有一个区别于其他的称作类标号(class label),类标号指示 元组所属的类。
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The traditional rough set theory can only deal with the discrete attribute in a database, so it is necessary to deal with the consistent attribute when the consistent attributes exist in a database.
传统的粗糙集理论只能对数据库中的离散属性进行处理,所以对存在连续属性的数据库必须进行离散化处理。
参考来源 - 粗糙集连续属性离散化方法研究·2,447,543篇论文数据,部分数据来源于NoteExpress
实际问题中经常涉及连续的数值属性,然而许多归纳学习算法却是针对离散属性空间的。
The continuous attribute problems are often encountered in the real world, but many outstanding inductive learning algorithms are mainly based on a discrete feature space.
由于粗糙集只能对离散属性进行处理,因而连续属性的离散化也就成了粗糙集的主要问题之一。
Because traditional rough set theory can only deal with the discrete attributes in database. So, the discretization of continuous attributes is one of the main problems in rough sets.
随后,我们讨论了SPRINT算法。 针对SPRINT算法的不足,提出了二种处理离散属性的新方法。
Next, We discuss SPRINT algorithm, and present two new methods to process categorical attributes.
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