Using a Variation of Chi2 to Simplify the Case Representation Space for Decision Tree Induction
Guiyun Zhang · Computer Engineering and Science · 2007
The simplification of training dataset representation and the generation of decision trees are two critical phases in decision tree induction.On the condition of bringing the inconsistency rate under a threshold,reducing the attribute number and the different value number of each attribute assures the feasibility and effectiveness of the decision tree learning method.In this paper,a variation of the Chi2 algorithm is proposed to perform attribute discretization and selection.The decision tree generated in the further steps offers a good classification accuracy.Our experiment is based on a data set donated by an insurance company from the real world.