基于依赖分类器的盲多类集成学习

Panagiotis A. Traganitis, G. Giannakis
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引用次数: 5

摘要

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,由于每种算法对不同类型的数据表现出不同的行为,因此人们有动机明智地融合多种算法,以便为给定的数据集找到“最佳”表现的算法。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合中学习的简单盲方案。盲指的是不知道每个分类器所训练的真值标签的组合者。虽然目前大多数工作都假设所有分类器都是独立的,但这项工作引入了一个可以处理分类器之间依赖关系的方案。对合成数据的初步测试显示了所提议方法的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blind Multi-class Ensemble Learning with Dependent Classifiers
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.
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