【摘要】 集成学习是提高分类精度的一种有效途径,已在许多领域显示出其优于单个分类器的良好性能。观察学习是一种基于社会学习理论的集成学习方法,以往对其研究集中于同构模式。在此提出了基于异构模式的观察学习策略,通过训练、观察、再训练三个阶段完成学习。在UCI标准数据集上对异构环境下的观察学习算法进行了实验研究。结果表明,该方法优于多数投票法和单个分类算法,其对弱分类器组成的分类器集合尤其有效。从偏差/方差分解的角度对观察学习提高分类性能的原因进行了论证,结果表明,观察学习算法既可以降低偏差,也可以降低方差。更多还原

【Abstract】 Ensemble method has shown the potential to increase classification accuracy beyond the level reached by an individual classifier alone.Observational Learning Algorithm (OLA) is an ensemble method based on social learning theory.Previous work mainly focused on OLA for homogeneous ensembles,such as neural networks ensembles.In this paper,OLA for heterogeneous ensembles was proposed,which is a process with three steps:training,observing,and retraining.Experiments on five datasets from the UCI repository show that,OLA outperforms the individual base learner and majority voting when base learners are not capable enough for the given task.Bias-variance decomposition of the error indicates that OLA can reduce both bias and variance.更多还原