Types of Cost in Inductive Concept Learning
Types of Cost in Inductive Concept Learning
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发表时间：
2002-12
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通讯作者：
Peter D. Turney
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作者：
Peter D. Turney
归纳概念学习是学习将案例分配到一组离散的班级中的任务。在概念学习的现实应用中，涉及到许多不同类型的成本。大多数机器学习文献忽略了所有类型的成本(除非准确性被解释为一种成本衡量标准)。一些论文对错误分类的代价进行了调查。很少有论文研究过许多其他类型的成本。在本文中，我们试图对归纳概念学习中涉及的不同类型的成本进行分类。这种分类可能有助于组织有关成本敏感型学习的文献。我们希望这能启发研究者更深入地研究归纳概念学习中的各种成本。
Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. Very few papers have examined the many other types of cost. In this paper, we attempt to create a taxonomy of the different types of cost that are involved in inductive concept learning. This taxonomy may help to organize the literature on cost-sensitive learning. We hope that it will inspire researchers to investigate all types of cost in inductive concept learning in more depth.