Improved ID3 decision tree algorithm based on rough set
ZHU Fu-bao
HUO Xiao-qi
XU Xian-jing
Abstract：The traditional decision tree algorithms such as ID3 usually uses a single attribute as the basis of branching judgment.The scale of the tree generated by ID3 is very large and rules formed are difficult to understand.Aiming at the problems described above,an algorithm was proposed using multi-variable as the judging conditions of node attributes.By using the property of attribute dependency in rough set and choo-sing nuclear properties of condition attributes relative to decision attributes in the information system as multi-variable node attributes,the algorithm used the concept of relative generalization to aid the branching process and generated a multi-variable decision tree.Through the analysis of example and by comparing with the conventional ID3 algorithm,the high efficiency of the improved algorithm was verified.
Keywords：rough setID3 algorithmdecision treerelative generalizationequivalent relationship
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 50-54 )