论文标题
用于构建决策树的合奏的进化算法
Evolutionary algorithms for constructing an ensemble of decision trees
论文作者
论文摘要
大多数决策树诱导算法基于贪婪的自上而下的递归分区策略，用于树木生长。在本文中，我们提出了几种基于进化算法诱导决策树及其集成的方法。我们方法的主要区别是使用决策树的实价矢量表示，允许使用大量不同的优化算法，并优化整个树或合奏以避免本地Optima。选择差异进化和进化策略是作为优化算法，因为它们在增强学习问题方面具有良好的结果。我们使用几个公共UCI数据集测试了这种方法的预测性能，并且所提出的方法比经典方法显示出更好的质量。
Most decision tree induction algorithms are based on a greedy top-down recursive partitioning strategy for tree growth. In this paper, we propose several methods for induction of decision trees and their ensembles based on evolutionary algorithms. The main difference of our approach is using real-valued vector representation of decision tree that allows to use a large number of different optimization algorithms, as well as optimize the whole tree or ensemble for avoiding local optima. Differential evolution and evolution strategies were chosen as optimization algorithms, as they have good results in reinforcement learning problems. We test the predictive performance of this methods using several public UCI data sets, and the proposed methods show better quality than classical methods.
