一种改进的自映射空间判定树算法

判定树在基于知识的专家系统中非常有用,同时在数据挖掘中也是一种重要的方法.但是目前的判定树判定方法并不能准确、清晰地处理与人类思想和感觉的知识.通过自映射空间模型作为知识表达和处理不确定性的方法以达到改进目前方法的目的.与传统的分类方法相比,自映射空间方法更好地集成了模糊性和随机性.提出了基于自映射空间模型的判定树方法,该方法处理人类思维更加自然.在实际的分类问题过程中,自映射空间方法更加有效、灵活.

An Improved Self-mapping Space Decision Tree Algorithm

The decision tree is very useful in building knowledge-based expert system, and it is also a powerful method in (spatial) data mining. But the current decision tree induction methods do not deal with vagueness and ambiguity associated with human thinking and perception very well. This paper presented a self-mapping space(SMS) model for knowledge representation and uncertainty handling. Compared with classical induction method, the SMS integrates the fuzziness and randomness of linguistic terms in a better way. A new kind of decision tree based on SMS model (SMS Decision Tree, denoted as SMS-DT) was developed, and the detailed induction method was presented. The method associates naturally with human thinking and perception. In a practical spatial classification problem, the SMS method shows the benefits in effectiveness and flexibility.