Advances and Trends in Artificial Intelligence. From Theory to Practice - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, July 9-11, 2019, Proceedings
Advances and Trends in Artificial Intelligence. From Theory to Practice - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, July 9-11, 2019, Proceedings
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人工智能的进展和趋势。
DOI：
10.1007/978-3-030-22999-3_54
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Ursani Z
中科院分区：
文献类型：
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作者：
Ursani Z
这是我们关于分类的分层学习系列论文中的第五篇。用于分类的分层学习是创建学习模型的分层列表的自动方法，所述分层列表一方面能够将训练集划分成相等数量的子集，另一方面也能够将每个相应子集的元素分类为问题的类别。本文对用于分类的概率分层学习进行了形式化描述，并将其作为一种理论给出。该理论认为，通过对低复杂度模型的分层应用，可以产生复杂数据集的准确模型。该理论通过在五个流行的真实世界数据集上的实验得到了验证。并对该理论的泛化能力进行了检验。与当代文学的比较表明，这一理论的前景是光明的。这一理论被四个公设所涵盖，这些公设通过数学形式主义优雅地刻画出来。
This is the 5th paper in our series of papers on hierarchical learning for classification. Hierarchical learning for classification is an automated method of creating hierarchy list of learnt models that are on the one hand capable of partitioning the training set into equal number of subsets and on the other hand are also capable of classifying elements of each corresponding subset into classes of the problem. In this paper, the probabilistic hierarchical learning for classification has been formalized and presented as a theory. The theory asserts that the accurate models of complex datasets can be produced through hierarchical application of low complexity models. The theory is validated through experiments on five popular real-world datasets. Generalizing ability of the theory is also tested. Comparison with the contemporary literature points towards promising future for this theory. The theory is covered by four postulates, which are carved out elegantly through mathematical formalisms.