基于有序决策树的改进归纳算法

An improved induction algorithm based on ordinal decision tree

基于构建有序决策树,提出了一种新的归纳算法。该算法选择的扩展属性不仅和类的有序互信息值最大,而且要求和同一分支上已被用过的条件属性的有序互信息值最小。实验结果表明,考虑了条件属性之间的相关性后,可避免同一条件属性的重复选择,真正体现了条件属性和决策属性之间的有序互信息,与已有的算法相比,提高了测试精度。

An improved ordinal decision tree algorithm was proposed.The extended attributes selected with the pro-posed algorithm maximized the ranking mutual information between the candidate attributes and the decision attribute, and also minimized the ranking mutual information between the candidate attributes and the selected conditional attrib-utes on the same branch.The experimental results showed that the correlation to be taken account among the conditional attributes could avoid to selecte the same one, and the ideas of the proposed method could really reflect the nature of the ranking mutual information.The proposed algorithm could improve the test accuracy compared with the existing algo-rithms.