Conceptual distance of numerically specified case features

Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems Pub Date : 1995-11-20 DOI:10.1109/ANNES.1995.499473

W. Dubitzky, A. Schuster, J. Hughes, D. Bell

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引用次数: 3

Abstract

Case-based reasoning (CBR) systems rely on the conceptual ordering of entities called cases. If atomic case features are allowed to assume numeric as well as symbolic values, then a systematic comparison regime is needed to aggregate similarity scores. A common approach to deal with real-numbered features is normalisation. However, there are two conspicuous problems with this procedure: the similarity between two features is dependent on the corresponding values of all other cases to be ranked; and real-numbered features are often interpreted by human experts according to conceptual constraints associated with features. In such situations, a conceptual distance between two features should be determined rather than the length of a 'gap' on a linear scale. Within the framework of a comprehensive case-knowledge architecture, the notion of a concept frame that can be associated with a case feature is proposed. Through this component it is possible to represent polymorphic atomic case features, and to systematically establish the concept distance between two real-numbered feature instances.

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数字指定案例特征的概念距离

基于案例的推理(CBR)系统依赖于称为案例的实体的概念排序。如果允许原子情况特征假设数字和符号值，那么就需要一个系统的比较机制来汇总相似性分数。处理实数特征的常用方法是规范化。然而，这个过程有两个明显的问题:两个特征之间的相似性依赖于所有其他待排序情况的对应值;实数特征通常由人类专家根据与特征相关的概念约束来解释。在这种情况下，应该确定两个特征之间的概念距离，而不是线性尺度上的“间隙”长度。在综合案例知识体系结构的框架内，提出了与案例特征相关联的概念框架的概念。通过该组件可以表示多态原子案例特征，并系统地建立两个实数特征实例之间的概念距离。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems

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