论文标题
基于两级多样性方法的分类器池生成
Classifier Pool Generation based on a Two-level Diversity Approach
论文作者
论文摘要
本文描述了一种分类器池生成方法，该方法由数据复杂性和分类器决策的多样性指导。首先，通过考虑数据集的几个子样本来评估复杂度度量的行为。选择在整个子样本之间具有较高可变性的复杂度度量以进行后池适应，其中进化算法在复杂性和决策空间中都优化了多样性。具有28个数据集和20个复制的强大实验协议用于评估所提出的方法。当应用动态分类器选择和动态集合选择方法时，结果显示了69.4％的实验的明显准确性提高。
This paper describes a classifier pool generation method guided by the diversity estimated on the data complexity and classifier decisions. First, the behavior of complexity measures is assessed by considering several subsamples of the dataset. The complexity measures with high variability across the subsamples are selected for posterior pool adaptation, where an evolutionary algorithm optimizes diversity in both complexity and decision spaces. A robust experimental protocol with 28 datasets and 20 replications is used to evaluate the proposed method. Results show significant accuracy improvements in 69.4% of the experiments when Dynamic Classifier Selection and Dynamic Ensemble Selection methods are applied.
