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Hierarchical classifier with overlapping class groups

机译：具有重叠类组的分层分类器

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In this paper a novel complex classifier architecture is proposed. The architecture has a hierarchical tree-like structure with simple artificial neural networks (ANNs) at each node. The actual structure for a given problem is not preset but is built throughout training. The training algorithm's ability to build the tree-like structure is based on the assumption that when a weak classifier (i.e., one that classifies only slightly better than a random classifier) is trained and examples from any two output classes are frequently mismatched, then they must carry similar information and constitute a sub-problem. After each ANN has been trained its incorrect classifications are analyzed and new sub-problems are formed. Consequently, new ANNs are built for each of these sub-problems and form another layer of the hierarchical classifier. An important feature of the hierarchical classifier proposed in this work is that the problem partition forms overlapping sub-problems. Thus, the classification follows not just a single path from the root, but may fork enhancing the power of the classification. It is shown how to combine the results of these individual classifiers.
机译：本文提出了一种新颖的复杂分类器架构。该体系结构具有分层的树状结构，在每个节点上都具有简单的人工神经网络（ANN）。给定问题的实际结构不是预设的，而是在整个培训过程中构建的。训练算法构建树状结构的能力基于以下假设：当训练弱分类器（即，仅比随机分类器分类的分类要好一些）并且任意两个输出类的示例频繁失配时，则它们必须携带类似的信息并构成子问题。训练完每一个人工神经网络后，将分析其不正确的分类并形成新的子问题。因此，将为这些子问题中的每一个建立新的ANN，并形成分层分类器的另一层。这项工作中提出的分层分类器的一个重要特征是，问题划分形成了重叠的子问题。因此，分类不仅遵循从根开始的单一路径，而且可以分叉提高分类的能力。它显示了如何合并这些单独分类器的结果。

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