Using mutual information for selecting continuous-valued attribute in decision tree learning
Hua Li, Xizhao Wang, Yong Li · 2004
In this paper, we proposed a learning algorithm using the information entropy minimization heuristic and mutual information entropy heuristic to select expanded attributes. For a data set of which the values of condition attributes are continuous, most of the current decision trees learning algorithms often select the previously selected attributes for branching. The repeated selection limits the accuracy of training and testing and the structure of decision trees may become complex. So in the selection of attributes, the previously selected attributes and the other attributes, which have high correlation to the previously selected attributes, should not be selected again. Here, we use mutual information to avoid selecting the previously selected attributes in the generation of decision trees and our test results show that this method can obtain good performance.