元学习算法选择机制及关联对性能的影响

提出一种元学习定义，从偏差/方差分解角度对元学习中学习算法的选取机制进行研究，得出了元级选用错误率低且偏差小的学习算法、基级学习算法按照错误率及方差从低到高排列的结论。鉴于标准数据集不能充分评估关联对元学习性能的影响，设计了一种模拟算法以产生模拟数据集。在UCI标准数据集和模拟数据集上的实验表明，同常用的多数投票等组合方法相比，基于算法选择机制的元学习表现出优良的性能，且分类器之间的负关联有助于性能的改进。

In this paper, a general definition of meta-learning is proposed. The selection of learning algorithms in meta-learning is investigated from the point of bias/variance decomposition as well as the effect of correlation on its accuracy. In order to obtain classifiers with variable correlation, artificial datasets are generated based on the simulating algorithm presented in the paper. Experiments are performed on UCI datasets and simulated datasets and show that meta-learning outperforms several combining methods averagely; and that negative correlation measured by Q statistic benefits meta-learning approach.