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首页> 外文期刊>Computational Intelligence >AN EXTENSIVE EVALUATION OF DECISION TREE-BASED HIERARCHICAL MULTILABEL CLASSIFICATION METHODS AND PERFORMANCE MEASURES
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AN EXTENSIVE EVALUATION OF DECISION TREE-BASED HIERARCHICAL MULTILABEL CLASSIFICATION METHODS AND PERFORMANCE MEASURES

机译：基于决策树的分层多标签分类方法和性能指标的广泛评价

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摘要

Hierarchical multilabel classification is a complex classification problem where an instance can be assigned to more than one class simultaneously, and these classes are hierarchically organized with superclasses and subclasses, that is, an instance can be classified as belonging to more than one path in the hierarchical structure. This article experimentally analyses the behavior of different decision tree-based hierarchical multilabel classification methods based on the local and global classification approaches. The approaches are compared using distinct hierarchy-based and distance-based evaluation measures, when they are applied to a variation of real multilabel and hierarchical datasets' characteristics. Also, the different evaluation measures investigated are compared according to their degrees of consistency, discriminancy, and indifferency. As a result of the experimental analysis, we recommend the use of the global classification approach and suggest the use of the Hierarchical Precision and Hierarchical Recall evaluation measures.
机译：分层多标签分类是一个复杂的分类问题，其中可以将一个实例同时分配给一个以上的类，并且这些类通过父类和子类进行分层组织，即，一个实例可以分类为属于该分层结构中的多个路径结构体。本文通过实验分析了基于局部和全局分类方法的不同基于决策树的分层多标签分类方法的行为。当将这些方法应用于真实的多标签和分层数据集特征的变体时，将使用不同的基于层次结构和基于距离的评估方法进行比较。而且，根据调查的不同评估方法的一致性，可区分性和冷漠程度进行比较。作为实验分析的结果，我们建议使用全局分类方法，并建议使用“层次精度”和“层次召回”评估措施。

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