CASE DISPATCHING VERSUS CASE-BASE MERGING: WHEN MCBR MATTERS

Author:

LEAKE DAVID B.1,SOORIAMURTHI RAJA2

Affiliation:

1. Computer Science Department, Lindley Hall 215, Indiana University, 150 S. Woodlawn Avenue, Bloomington, Indiana 47405, U.S.A.

2. Kelley School of Business, Business 540D, Indiana University, 1309 East 10th Street, Bloomington, Indiana 47405, U.S.A.

Abstract

Multi-case-base reasoning (MCBR) extends case-based reasoning to draw on multiple case bases that may address somewhat different tasks. In MCBR, an agent selectively supplements its own case-base as needed, by dispatching problems to external case-bases and using cross-case-base adaptation to adjust their solutions for inter-case-base differences. MCBR has been advocated as a means to facilitate handling large case-bases when storage is limited, or to enable use of distributed case sources. However, this raises an important question: When storage is not an issue, and the cases from all external case sources could be merged into a single case-base, is there any reason for MCBR? This article answers that question with an experimental assessment of how MCBR affects the quality of solutions generated. It demonstrates that for a given local case-base and an external case-base for a task environment that is similar to, but different from, the local task environment, MCBR can improve accuracy compared to merging the case-bases into a single case-base. This improvement holds even if the cross-case-base adaptation method used by MCBR is also applied to the external cases before merging. The article hypothesizes an explanation of this behavior in terms of the ability of MCBR to exploit the tradeoffs between similarity of problems and similarity of solution contexts. It provides experimental evidence to support this hypothesis, and also demonstrates that MCBR is a useful framework for guiding case-base maintenance by selecting cases to add to a case-base.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach;Lecture Notes in Computer Science;2024

2. Study on Case-Based Reasoning-Inspired Approaches to Machine-Learning;2015 International Conference on Intelligent Transportation, Big Data and Smart City;2015-12

3. An Agent Based Framework for Multiple, Heterogeneous Case Based Reasoning;Case-Based Reasoning Research and Development;2013

4. Ensembles of case-based reasoning classifiers in high-dimensional biological domains;Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery;2011-02-18

5. An analysis of data distribution in the ClassAge system: An agent-based system for classification tasks;Neurocomputing;2008-10

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