集成学习算法的比较研究

Comparative Study for Ensemble Learning Algorithms

从差异性出发,研究了基于特征技术与数据技术的集成学习算法,深入分析了这些集成学习算法产生差异性的方法;针对决策树与神经网络模型在标准数据集对集成学习算法进行了实验研究,结果表明集成学习算法的性能依赖于数据集的特性以及产生差异性的方法等因素,并且基于数据的集成学习算法的性能优于基于特征集的集成学习算法的性能.

From point of view of diversity,ensemble learning algorithms based on feature set and data technique are studied.Methods of creating diversity for these ensemble learning algorithms are deeply analyzed.And experimental studies for using decision trees and neural networks as basis models are conducted on 10 standard data sets.They show that performances of ensemble learning algorithms depend on character of data set,method of creating diversity,and etc.Furthermore,performances of ensemble learning algorithms...