Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms

This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algo-rithms. Top-down decision-tree algorithms are of great importance, considering their ability of providing an intuitive and accurate knowledge representation for classifi-cation problems. The automatic design of these algorithms seems timely, given the large literature accumulated in more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary al-gorithm, HEAD-DT, is extensively tested using 20 public UCI data sets and 10 real-world microarray gene expression data sets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART