论文标题
通过多样性约束的整数编程方法进行整体修剪
Ensemble pruning via an integer programming approach with diversity constraints
论文作者
论文摘要
合奏学习结合了多个分类器，以期获得更好的预测性能。经验研究表明，与使用所有分类器相比，合奏修剪（即选择适当的可用分类器子集）可以带来可比或更好的预测。在本文中，我们考虑了二进制分类问题，并提出了选择最佳分类器子集的整数编程方法（IP）方法。我们提出一个灵活的目标函数，以适应不同数据集的所需标准。我们还提出了限制，以确保合奏中的最低多样性水平。尽管IP的总体情况是NP-HARD，但最先进的求解器仍能够快速获得具有多达60000个数据点的数据集的良好解决方案。与文献中一些最佳和最常用的修剪方法相比，我们的方法会产生竞争结果。
Ensemble learning combines multiple classifiers in the hope of obtaining better predictive performance. Empirical studies have shown that ensemble pruning, that is, choosing an appropriate subset of the available classifiers, can lead to comparable or better predictions than using all classifiers. In this paper, we consider a binary classification problem and propose an integer programming (IP) approach for selecting optimal classifier subsets. We propose a flexible objective function to adapt to desired criteria of different datasets. We also propose constraints to ensure minimum diversity levels in the ensemble. Despite the general case of IP being NP-Hard, state-of-the-art solvers are able to quickly obtain good solutions for datasets with up to 60000 data points. Our approach yields competitive results when compared to some of the best and most used pruning methods in literature.
