Case-based Reasoning Community Research Articles

Integrating a case-based reasoning shell and Web 2.0: design recommendations and insights

The design and implementation of case-based reasoning (CBR) applications is time-consuming. To facilitate the development of CBR applications in various problem domains, the CBR community has created a number of CBR shells and software frameworks in the past twenty years. This paper provides a review of the state-of-the-art of CBR shells and software frameworks, highlights why the integration of Web 2.0 and CBR development tools is useful, and gives an example as to how we implement such integration. We use this example to illustrate how Web 2.0 features such as blogging functions can be integrated in a CBR system. Design recommendations and insights for implementing a Web 2.0-based CBR shell are also provided.

Report on the Twenty‐Second International Conference on Case‐Based Reasoning

In cooperation with the Association for the Advancement of Artificial Intelligence (AAAI), the Twenty-Second International Conference on Case-Based Reasoning (ICCBR), the premier international meeting on research and applications in case-based reasoning (CBR), was held from Monday September 29 to Wednesday October 1, 2014, in Cork, Ireland. ICCBR is the annual meeting of the CBR community and the leading conference on this topic. Started in 1993 as the European Conference on CBR and 1995 as ICCBR, the two conferences alternated biennially until their merger in 2010.

Report on the Twenty‐First International Conference on Case‐Based Reasoning

In cooperation with the Association for the Advancement of Artificial Intelligence (AAAI), the twenty‐first International Conference on Case‐Based Reasoning (ICCBR), the premier international meeting on research and applications in case‐based reasoning (CBR), was held in July 2013 in Saratoga Springs, NY. ICCBR is the annual meeting of the CBR community and the leading conference on this topic. This year ICCBR featured the Industry Day, the fifth annual Doctoral Consortium and three workshops.

An application of a GA with Markov network surrogate to feature selection

Surrogate models of fitness have been presented as a way of reducing the number of fitness evaluations required by evolutionary algorithms. This is of particular interest with expensive fitness functions where the time taken for building the model is outweighed by the savings of using fewer function evaluations. In this article, we show how a Markov network model can be used as a surrogate fitness function for a genetic algorithm in a new algorithm called Markov Fitness Model Genetic Algorithm (MFM-GA). We thoroughly investigate its application to a fitness function for feature selection in Case-Based Reasoning (CBR), using a range of standard benchmarks from the CBR community. This fitness function requires considerable computation time to evaluate and we show that using the surrogate offers a significant decrease in total run-time compared to a GA using the true fitness function. This comes at the cost of a reduction in the global best fitness found. We demonstrate that the quality of the solutions obtained by MFM-GA improves significantly with model rebuilding. Comparisons with a classic GA, a GA using fitness inheritance and a selection of filter selection methods for CBR shows that MFM-GA provides a good trade-off between fitness quality and run-time.

Case-Based Reasoning

The 1994 Workshop on Case-Based Reasoning (CBR) focused on the evaluation of CBR theories, models, systems, and system components. The CBR community addressed the evaluation of theories and implemented systems, with the consensus that a balance between novel innovations and evaluations could maximize progress.