In some variants of the supervised classification setting, the domains of the attributes and the set of classes are totally ordered sets. The task of learning a classifier that is nondecreasing w.r.t. each attribute is called monotonic classification. Several kinds of models can be used in this task; in this paper , we focus on decision rules. We propose a method for learning a set of decision rules that optimally fits the training data while favoring short rules over long ones. We give new results on the representation of sets of if-then rules by extensions of Sugeno integrals to distinct attribute domains, where local utility functions are used to map attribute domains to a common totally ordered scale. We study whether such qualitative extensions of Sugeno integral provide compact representations of large sets of decision rules.

中文翻译：

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单调分类问题的学习规则集和 Sugeno 积分

在监督分类设置的一些变体中，属性的域和类集是完全有序的集。学习一个与每个属性都非递减的分类器的任务称为单调分类。在这个任务中可以使用几种模型；在本文中，我们关注决策规则。我们提出了一种学习一组决策规则的方法，这些规则最适合训练数据，同时有利于短规则而不是长规则。我们通过将 Sugeno 积分扩展到不同的属性域来给出 if-then 规则集表示的新结果，其中局部效用函数用于将属性域映射到一个共同的全序尺度。我们研究 Sugeno 积分的这种定性扩展是否提供了大量决策规则的紧凑表示。