提出一種元學習定義，從偏差/方差分解角度對元學習中學習算法的選取機制進行研究，得出了元級選用錯誤率低且偏差小的學習算法、基級學習算法按照錯誤率及方差從低到高排列的結論。鑒于標準數據集不能充分評估關聯對元學習性能的影響，設計了一種模擬算法以產生模擬數據集。在UCI標準數據集和模擬數據集上的實驗表明，同常用的多數投票等組合方法相比，基于算法選擇機制的元學習表現出優良的性能，且分類器之間的負關聯有助于性能的改進。

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.