Blind Multi-class Ensemble Learning with Dependent Classifiers
Blind Multi-class Ensemble Learning with Dependent Classifiers
复制标题
具有相关分类器的盲多类集成学习
DOI：
10.23919/eusipco.2018.8553113
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
G. Giannakis
中科院分区：
文献类型：
--
作者：
Panagiotis A. Traganitis;G. Giannakis
近年来，模式识别和数据分析的进步刺激了大量机器学习算法和工具的发展。然而，由于每个算法对于不同类型的数据表现出不同的行为，人们被激励明智地融合多个算法，以便为给定的数据集找到性能最佳的一个。集成学习旨在通过组合多个算法的输出来创建这样一个高性能的元学习器。本文介绍了一种从分类器集成中学习的简单盲方案。盲是指对每个分类器都训练过的基本事实标签一无所知的组合器。虽然目前的大多数工作都假设所有的分类器都是独立的，但本工作引入了一种能够处理分类器之间的依赖关系的方案。对合成数据的初步测试展示了所提出的方法的潜力。
In recent years, advances in pattern recognition and data analytics have spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm exhibits different behavior for different types of data, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims to create such a high-performance meta-learner, by combining the outputs from multiple algorithms. The present work introduces a simple blind scheme for learning from ensembles of classifiers. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. While most current works presume that all classifiers are independent, this work introduces a scheme that can handle dependencies between classifiers. Preliminary tests on synthetic data showcase the potential of the proposed approach.
DOI：
10.1007/3-540-45014-9
发表时间：
2000-06
期刊：
--
影响因子：
--
作者：
Thomas G. Dietterich
通讯作者：
Thomas G. Dietterich