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Enhancing the Effectiveness of Interactive Case-Based Reasoning with Clustering and Decision Forests

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Abstract

In interactive case-based reasoning, it is important to present a small number of important cases and problem features to the user at one time. This goal is difficult to achieve when large case bases are commonplace in industrial practice. In this paper we present our solution to the problem by highlighting the interactive user- interface component of the CaseAdvisor system. In CaseAdvisor, decision forests are created in real time to help compress a large case base into several small ones. This is done by merging similar cases together through a clustering algorithm. An important side effect of this operation is that it allows up-to-date maintenance operations to be performed for case base management. During the retrieval process, an information-guided subsystem can then generate decision forests based on users' current answers obtained through an interactive process. Possible questions to the user are carefully analyzed through information theory. An important feature of the system is that case-base maintenance and reasoning are integrated in a seamless whole. In this article we present the system architecture, algorithms as well as empirical evaluations.

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Authors and Affiliations

  1. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, V5A 1S6

    Qiang Yang

  2. AMDOCS Limited, Mount Pleasant Rd, Toronto, Canada

    Jing Wu

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  1. Qiang Yang
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Yang, Q., Wu, J. Enhancing the Effectiveness of Interactive Case-Based Reasoning with Clustering and Decision Forests. Applied Intelligence 14, 49–64 (2001). https://doi.org/10.1023/A:1008303008006

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