Universidade de São Paulo, São Carlos, Brazil
Evol Comput. 2013 Winter;21(4):659-84. doi: 10.1162/EVCO_a_00101. Epub 2013 Aug 8.
This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. 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.
本研究报告了一种超启发式进化算法的实证分析，该算法能够自动设计自上而下的决策树归纳算法。自上而下的决策树算法非常重要，因为它们能够为分类问题提供直观而准确的知识表示。考虑到在手动设计决策树归纳算法方面积累了 40 多年的大量文献，自动设计这些算法似乎很及时。所提出的超启发式进化算法 HEAD-DT 使用 20 个公共 UCI 数据集和 10 个微阵列基因表达数据集进行了广泛测试。HEAD-DT 自动设计的算法与传统的决策树归纳算法（如 C4.5 和 CART）进行了比较。实验结果表明，HEAD-DT 能够生成比 C4.5 和 CART 更准确的算法。
