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Temporal Similarity By Measuring Possibilistic Uncertainty In Cbr

机译：通过测量CBR中的可能性不确定性来进行时间相似

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摘要

Similarity is an essential concept in case-based reasoning (CBR). In domains in which time plays a relevant role, CBR systems require good temporal similarity measures to compare cases. Temporal cases are traditionally represented by a set of temporal features, defining time series and temporal event sequences. In the particular situation where these features are not homogeneous (i.e. combination of qualitative and quantitative information), systems find difficulties in performing the CBR cycle. Furthermore, temporal similarity measures cannot directly apply the efficient time series techniques, requiring new approaches to deal with these heterogeneous sequences. To this end, recent proposals are focused on direct matching between pairs of features within sequences, mainly based on classical distances. However, three limitations to the traditional approaches have been identified: (1) they do not consider the implicit temporal relations amongst all features of the sequence (ignoring a large amount of temporal information); (2) they ignore the uncertainty produced in any process of analogy; (3) they are designed to compare pairs of sequences, limiting their use to basic aspects of the Retrieval step of CBR (no benefits on other CBR steps). Temporal constraint networks have proved to be useful tools for temporal representation and reasoning, and can be easily extended to manage imprecision and uncertainty. An approach to solve similarity problems could be the transformation of these heterogeneous sequences into uncertain temporal relations, obtaining a temporal constraint network. The overall uncertainty of this network can be considered as an effective indicator of the sequences similarity. Therefore, this paper proposes a non-classical approach to measure temporal similarity of cases which are heterogeneous temporal event sequences. Given two or more sequences, the temporal similarity is measured by describing a unique temporal scenario of possibilistic temporal relations and calculating the uncertainty produced.
机译：在基于案例的推理（CBR）中，相似性是必不可少的概念。在时间起着重要作用的领域中，CBR系统需要良好的时间相似性度量来比较案例。传统上，时间案例由一组时间特征表示，这些时间特征定义了时间序列和时间事件序列。在这些特征不均一的特定情况下（即定性和定量信息的组合），系统在执行CBR循环时会遇到困难。此外，时间相似性度量不能直接应用有效的时间序列技术，需要新的方法来处理这些异构序列。为此，最近的提议集中在序列上的特征对之间的直接匹配上，主要基于经典距离。但是，已经确定了传统方法的三个局限性：（1）它们没有考虑序列所有特征之间的隐式时间关系（忽略了大量的时间信息）； （2）他们忽略了任何类比过程中产生的不确定性； （3）它们被设计为比较序列对，将它们的使用限制为CBR检索步骤的基本方面（其他CBR步骤没有任何好处）。时间约束网络已被证明是用于时间表示和推理的有用工具，并且可以轻松扩展以管理不精确性和不确定性。解决相似性问题的一种方法可能是将这些异类序列转换为不确定的时间关系，从而获得时间约束网络。该网络的总体不确定性可以被认为是序列相似性的有效指标。因此，本文提出了一种非经典的方法来测量时间异质性事件案例的时间相似性。给定两个或多个序列，通过描述可能的时间关系的唯一时间场景并计算产生的不确定性来测量时间相似性。

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