A multi-criterion pruning method for decision trees and its application in intrusion detection

To improve the applicability of decision trees, a multi-criterion pruning method was proposed for the application of decision trees in intrusion detection, which enabled decision trees suitable for different conditions by parameter adjustment. Several parameters for describing the performance of a decision tree, such as stability, complexity and classification ability, were proposed. To meet the needs of different applications, the decision tree was expressed as a vector. Weights of different components of the vector could be adjusted according to the fact, and the required decision tree could be built gradually. Experimental results show that the proposed method can rapidly construct different decision trees according different specific environments, thus one program can be used in different conditions. The approach changes the creator of a decision tree from a programmer to a user, so the program is more suitable and the result is more reasonable.