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Multi-dimensional Bayesian network classifiers: A survey
Artificial Intelligence Review ( IF 12.0 ) Pub Date : 2020-07-11 , DOI: 10.1007/s10462-020-09858-x Santiago Gil-Begue , Concha Bielza , Pedro Larrañaga
Artificial Intelligence Review ( IF 12.0 ) Pub Date : 2020-07-11 , DOI: 10.1007/s10462-020-09858-x Santiago Gil-Begue , Concha Bielza , Pedro Larrañaga
Multi-dimensional classification is a cutting-edge problem, in which the values of multiple class variables have to be simultaneously assigned to a given example. It is an extension of the well known multi-label subproblem, in which the class variables are all binary. In this article, we review and expand the set of performance evaluation measures suitable for assessing multi-dimensional classifiers. We focus on multi-dimensional Bayesian network classifiers, which directly cope with multi-dimensional classification and consider dependencies among class variables. A comprehensive survey of this state-of-the-art classification model is offered by covering aspects related to their learning and inference process complexities. We also describe algorithms for structural learning, provide real-world applications where they have been used, and compile a collection of related software.
中文翻译:
多维贝叶斯网络分类器:调查
多维分类是一个前沿问题,其中多个类变量的值必须同时分配给给定的示例。它是众所周知的多标签子问题的扩展,其中类变量都是二元的。在本文中,我们回顾并扩展了适用于评估多维分类器的性能评估措施集。我们专注于多维贝叶斯网络分类器,它直接处理多维分类并考虑类变量之间的依赖关系。通过涵盖与其学习和推理过程复杂性相关的方面,对这种最先进的分类模型进行了全面调查。我们还描述了结构学习算法,提供了使用它们的实际应用程序,
更新日期:2020-07-11
中文翻译:
多维贝叶斯网络分类器:调查
多维分类是一个前沿问题,其中多个类变量的值必须同时分配给给定的示例。它是众所周知的多标签子问题的扩展,其中类变量都是二元的。在本文中,我们回顾并扩展了适用于评估多维分类器的性能评估措施集。我们专注于多维贝叶斯网络分类器,它直接处理多维分类并考虑类变量之间的依赖关系。通过涵盖与其学习和推理过程复杂性相关的方面,对这种最先进的分类模型进行了全面调查。我们还描述了结构学习算法,提供了使用它们的实际应用程序,