一种基于叠加框架的元学习策略

A Meta-learning Strategy Based on Stacking Framework

本文讨论了分类器组合的叠加法以及相应的叠加策略，提出了一种新的基于叠加法的元学习策略。该策略对基分类器的预测结果进行投票表决，将表决的结果作为1层训练数据。实验结果表明，该方法比简单平均化验概率法的分类效果要好。

This paper describes stacking method and relative strategies, and proposes a new meta-learning strategy based on stacking framework. It votes the predictive results of basic classifier, and then takes the voting results as training data of level-1. Experiment results show that this approach can obtain more definite information from classifier outputs and achieves better classification effect than the weighted approach based on averaging posterior probability.