论文标题
基于小组决策的合奏学习框架
An ensemble learning framework based on group decision making
论文作者
论文摘要
分类问题是机器学习中的一个重要主题，旨在教机器如何按特定标准将数据组合在一起。在本文中，已经提出了基于小组决策（GDM）的集合学习框架（EL）方法来解决此问题。在此框架中，可以将基础学习者视为决策者，可以将不同的类别视为替代方案，可以将不同的基础学习者获得的分类结果视为绩效评级，并且可以使用分类方法的性能的精度，回忆和准确性来识别GDM中决策者的权重。此外，考虑到二进制分类问题中定义的精度和召回问题不能直接在多分类问题中使用，因此提出了一个VS REST（OVR）来获得每个类别的基础学习者的精确度和回忆。实验结果表明，在大多数情况下，基于GDM的提出的EL方法比其他6个流行分类方法具有更高的精度，这验证了所提出方法的有效性。
The classification problem is a significant topic in machine learning which aims to teach machines how to group together data by particular criteria. In this paper, a framework for the ensemble learning (EL) method based on group decision making (GDM) has been proposed to resolve this issue. In this framework, base learners can be considered as decision-makers, different categories can be seen as alternatives, classification results obtained by diverse base learners can be considered as performance ratings, and the precision, recall, and accuracy which can reflect the performances of the classification methods can be employed to identify the weights of decision-makers in GDM. Moreover, considering that the precision and recall defined in binary classification problems can not be used directly in the multi-classification problem, the One vs Rest (OvR) has been proposed to obtain the precision and recall of the base learner for each category. The experimental results demonstrate that the proposed EL method based on GDM has higher accuracy than other 6 current popular classification methods in most instances, which verifies the effectiveness of the proposed method.
