ABSTRACT Information extraction from an L-fuzzy context becomes a hard problem when we work with a large set of objects and/or attributes. The goal of this paper is to present two different and complementary techniques to reduce the size of the context. First, using overlap indexes, we will establish rankings among the elements of the context that will allow us to determine those that do not provide relevant information and eliminate them. Second, by means of Choquet integrals, we will aggregate some objects or attributes of the context in order to jointly use the provided information. One interesting application of the developed theory consists on helping in the differential diagnoses of diseases that share a large number of symptoms and, therefore, that are difficult of distinguish.

中文翻译：

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减少 L 模糊上下文的大小。疾病鉴别诊断工具

摘要当我们处理大量对象和/或属性时，从 L 模糊上下文中提取信息成为一个难题。本文的目标是提出两种不同且互补的技术来减少上下文的大小。首先，使用重叠索引，我们将在上下文元素之间建立排名，这将使我们能够确定那些不提供相关信息的元素并消除它们。其次，通过 Choquet 积分，我们会聚合上下文的一些对象或属性，以便共同使用提供的信息。已发展理论的一个有趣应用在于帮助对具有大量症状并因此难以区分的疾病进行鉴别诊断。