论文标题
数值数据的粗糙拓扑
The Rough Topology for Numerical Data
论文作者
论文摘要
在本文中，我们通过根据属性值对对象进行分类，将粗糙的拓扑和核心概括为数值数据。讨论了一种寻找数值数据核心的新方法。然后进行测量，以查找属性是否在核心中给出。这种寻找核心的新方法用于减少属性。通过使用八种不同的机器学习算法对其进行测试和比较。同样，讨论了如何使用该材料来对数据分类中属性的重要性进行排名。最后，还提供了将数据转换为相关数据并找到核心的算法和代码。
In this paper, we generalize the rough topology and the core to numerical data by classifying objects in terms of the attribute values. A new approach to finding the core for numerical data is discussed. Then a measurement to find whether an attribute is in the core or not is given. This new method for finding the core is used for attribute reduction. It is tested and compared by using eight different machine-learning algorithms. Also, it is discussed how this material is used to rank the importance of attributes in data classification. Finally, the algorithms and codes to convert data to pertinent data and to find the core is also provided.
