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中文摘要：
个体学习器的差异度是集成学习中的关键因素。流行的集成学习算法如Bagging通过重取样技术产生个体学习器的差异度。选择性集成从集成学习算法产生的个体学习器中选择一部分来集成,结果表明比原集成更好。但如何选择学习器是个难题。使用Q统计量度量两个学习器的差异度,提出一种新的决策树选择性集成学习方法。与C4.5,Bagging方法相比,表现出很好的效果。
英文摘要：
Diversity among the individual learners is deemed to be a key issue in ensemble learning.Popular ensemble learning algorithms such that Bagging adopts re-sampling technology to produce the diversity of the individual learners.Selective ensemble chooses individual learners which are a part of the original learners to ensemble.The result shows that it is better than the original ensemble.However,how to choose learners is a problem.Using Q statistic to measure the diversity of a pair of learners,a new selective ensemble learning method for decision tree is proposed.Compared with the C4.5 and Bagging method,it works better.
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