Abstract—Decision trees have been well studied, widely used in knowledge discovery and decision support systems. They are simple and practical prediction models but often suffer from excessive complexity and can even be incomprehensible. In this study, a genetic algorithm is used to construct decision trees of increased accuracy and efficiency compared to those constructed by the conventional ID3 or C4.5 decision tree building algorithms. An improved definition of an efficient binary decision tree is proposed and evaluated – instead of simply using the number of nodes in a tree, the average number of questions asked in the tree for all the database entries is proposed.

Index Terms—binary decision trees, genetic algorithm and DVT

Cite: Christopher Nwosisi, "A Novel Definition of an Efficient Binary Decision Tree," Lecture Notes on Information Theory, Vol. 3, No. 1, pp. 35-41, June 2015. doi: 10.18178/lnit.3.1.35-41