The decision tree is a usual method of classification in data mining.In the process of constructing a decision tree,the criteria of selecting attributes to split will influence the efficiency of classification directly.The decision tree algorithm traditionally is based on information theory measure.Presented a new algorithm for classification rules extraction by choosing attributes of importance of attributes and dependance based on rough set.Using this algorithm,can extract crisp rules from classification information system.Compared with the traditional ID3 algorithm,it's simpler in the structure,and can improve the efficiency of classification.

Rough set theory is a new mathematical tool to deal with vagueness and uncertainty. And now it has been widely applied in constructing decision tree which has no hierarchical attributes inside. However, hierarchical attributes exist generally in realistic environment, which leads that decision making has max rules. Using max rules to build decision trees can optimize decision trees and has practical values as well. So, in order to deal with hierarchical attributes in decision tree, this paper try to design an optimize algorithm of decision tree based on rough sets hierarchical attributes (ARSHA), which works by combining the hierarchical attribute values and deleting the associated objects when max rules exist in decision table. So that the algorithm developed in this paper can abstract the simplest rule set that can cover all information for decision making. Finally, a real example is used to demonstrate its feasibility and efficiency.

A Rule Abstracting Algorithm of Decision Tree Based on Rough Set

Optimize Algorithm of Decision Tree Based on Rough Sets Hierarchical Attributes

An optimized decision tree algorithm based on rough sets model is proposed. Firstly the most popular decision tree algorithms, which are based rough sets model, usually partition examples too detailed to avoid the negative impact caused by a few special examples on decision tree because of the classification accuracy. The inhibitory factors are put forward in the forming process of the algorithm to cut branches for decision tree, avoiding redundant steps of cutting branches later. Secondly the condition attribute and decision attribute are matched in every division to avoid unnecessary calculation and improve the efficiency of the algorithm.

A Rule Abstracting Algorithm of Decision Tree Based on Rough Set

Citations

Optimize Algorithm of Decision Tree Based on Rough Sets Hierarchical Attributes

An Optimized Algorithm of Decision Tree Based on Rough Sets Model

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