摘要
ID3决策树算法是构造决策树的重要算法之一,然而实验表明该算法在选择分裂属性时存在着多值偏向问题。以往的大多数学者都是基于实验分析多值偏向问题。该文针对这个问题,基于粗糙集理论及凹函数性质,引入函数重要度概念,从理论上分析多值偏向问题,并分析了属性多值对属性的重要度的影响。最后实验验证该理论分析方法的正确性与可行性。
As one of the classical algorithms for building a decision tree,the ID3,has a problem of tending to choose the attribute which has more different values. This problem is also called multi-value bias. Most scholars used to research the multi-value bias based on the experiment. To address this problem,this paper,based on rough set theory and concave function,introduces a concept called attribute importance concept,presents a theoretical analysis of the multi-value bias problem. This paper further analyzes the influence of attribute＇s multi-value on the other attribute. Lastly,the result of experimental proves this theoretical method correct and feasible.
出处
《杭州电子科技大学学报（自然科学版）》
2014年第2期41-44,共4页
Journal of Hangzhou Dianzi University：Natural Sciences
关键词
决策树算法
粗糙集
信息增益
多值偏向
属性重要度
decision algorithm
rough set
information gain
multi-value bias
attributes importance index
