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Evolving interval-based representation for multiple classifier fusion
Knowledge-Based Systems ( IF 7.2 ) Pub Date : 2020-05-16 , DOI: 10.1016/j.knosys.2020.106034
Tien Thanh Nguyen , Manh Truong Dang , Vimal Anand Baghel , Anh Vu Luong , John McCall , Alan Wee-Chung Liew

Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation. In the former, the optimal set of base classifiers, meta-classifier, original features, or meta-data is selected to obtain a better ensemble than using the entire classifiers and features. In the latter, the base classifiers or combining algorithms working on the outputs of the base classifiers are made to adapt to a particular problem. The adaptation here means that the parameters of these algorithms are trained to be optimal for each problem. In this study, we propose a novel evolving combining algorithm using the adaptation approach for the ensemble systems. Instead of using numerical value when computing the representation for each class, we propose to use the interval-based representation for the class. The optimal value of the representation is found through Particle Swarm Optimization. During classification, a test instance is assigned to the class with the interval-based representation that is closest to the base classifiers’ prediction. Experiments conducted on a number of popular dataset confirmed that the proposed method is better than the well-known ensemble systems using Decision Template and Sum Rule as combiner, L2-loss Linear Support Vector Machine, Multiple Layer Neural Network, and the ensemble selection methods based on GA-Meta-data, META-DES, and ACO.



中文翻译:

基于进化间隔的表示法用于多分类器融合

设计分类器集合是机器学习中的热门研究主题之一,因为与使用每个组成成员相比,它可以提供更好的结果。此外,可以使用选择或改编来提高合奏的性能。在前者中,与使用整个分类器和功能相比,选择最佳的基础分类器,元分类器,原始特征或元数据集可以获得更好的集成度。在后者中,使基本分类器或在基本分类器的输出上工作的组合算法适应于特定问题。这里的适应意味着将这些算法的参数训练为对于每个问题都是最优的。在这项研究中,我们提出了一种适用于整体系统的新颖的进化组合算法。我们建议在计算类的表示时不要使用数值,而建议对类使用基于间隔的表示。通过粒子群优化找到表示的最佳值。在分类期间,将使用最接近基本分类器预测的基于间隔的表示法将测试实例分配给该类。在许多受欢迎的数据集上进行的实验证实,该方法优于以决策模板和求和规则作为组合器,L2-损失线性支持向量机,多层神经网络以及基于该方法的集成选择方法的知名集成系统。关于GA元数据,META-DES和ACO。通过粒子群优化找到表示的最佳值。在分类期间,将使用最接近基本分类器预测的基于间隔的表示法将测试实例分配给该类。在许多受欢迎的数据集上进行的实验证实,该方法优于以决策模板和求和规则作为组合器,L2-损失线性支持向量机,多层神经网络以及基于该方法的集成选择方法的知名集成系统。关于GA元数据,META-DES和ACO。通过粒子群优化找到表示的最佳值。在分类期间,将使用最接近基本分类器预测的基于间隔的表示法将测试实例分配给该类。在许多受欢迎的数据集上进行的实验证实,该方法优于以决策模板和求和规则作为组合器,L2-损失线性支持向量机,多层神经网络以及基于该方法的集成选择方法的知名集成系统。关于GA元数据,META-DES和ACO。

更新日期:2020-05-16
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