An Algorithms to Construct Decision Tree Based on Information Entropy
Shucheng Huang · Journal of Changzhou Institute of Technology · 2006
Information theory provides us what mathematic essence of information is and borrows Entropy concept from thermodynamics to quantify information.Classification plays a big role in data mining.The key of constructing classifier tree based on training set is how to identify the test attribute for each internal node of the tree.Traditional algorithm of decision tree utilizes Entropy to address the issue,which trend to bias towards attributes with more different values.In this paper,we examine some concepts relating to Entropy and discuss their applications in classification tree problem of data mining, and design a decision tree based on mutual information,which eliminates the disadvantages with the old algorithms.