A new decision-tree classification algorithm for machine learning
Pei-Lei Tu, Ji-Moon Chung · 2003
Although decision-tree classification algorithms have been widely used for machine learning in artificial intelligence, there has been little research toward evaluating the performance or quality of the current classification algorithms and investigating the time and computational complexity of constructing the smallest size decision tree which best distinguishes characteristics of multiple distinct groups. A known NP-complete problem, 3-exact cover, is used to prove that this problem is NP-complete. One prevalent classification algorithm in machine learning, ID3, is evaluated. The greedy search procedure used by ID3 is found to create anomalous behavior with inferior decision trees on a lot of occasions. A decision-tree classification algorithm, the intelligent decision-tree algorithm (IDA), that overcomes these anomalies with better classification performance is presented. A time analysis shows that IDA is more computationally efficient than ID3, and a simulation study indicates that IDA outperforms ID3.>