The study of methods for post-pruning decision trees based on comprehensive evaluation standard

Abstract

Post-pruning is a common method of decision tree pruning. However, various post-pruning tends to use a single measure as an evaluation standard of pruning effects. The single and exclusive index evaluation standard of decision tree is subjective and partial, and the decisions after pruning often have a bias. This paper proposes a decision tree post-pruning algorithm based on comprehensive considering various evaluation standards. At the same time considering the classification ability, stability and size, so as to reflect the integrity advantage of the decision tree. The user can choose each standard component weight value according to actual demand, to get a decision tree which has a tendency to meet the actual demand. The experimental results show that the post-pruning algorithm considering the classification accuracy, stability and the size of decision tree, in classification accuracy unchanged or fall under the premise of tiny range, makes a decision tree has a more balanced classification performance and less model complexity.