CopyrightItems in 'OpenAIR@RGU', Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in 'OpenAIR@RGU' infringes copyright, please contact openair-help@rgu.ac.uk with details. The item will be removed from the repository while the claim is investigated.The final publication is available at Springer via http://dx.doi.org/10.1007/s10844-015-0378-z Case-Base Maintenance with Multi-Objective Evolutionary AlgorithmsAbstract Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solving ability of the Case-Base Reasoning system. However, when the case-base has many cases, then performance problems arise due to the time needed to find those similar cases to the input problem. At this point, Case-Base Maintenance algorithms can be used to reduce the number of cases and maintain the accuracy of the Case-Base Reasoning system at the same time. Whereas Case-Base Maintenance algorithms typically use a particular heuristic to remove (or select) cases from the case-base, the resulting maintained case-base relies on the proportion of redundant and noisy cases that are present in the case-base, among other factors. That is, a particular Case-Base Maintenance algorithm is suitable for certain types of case-bases that share some indicators, such as redundancy and noise levels.In the present work, we consider Case-Base Maintenance as a multi-objective optimization problem, which is solved with a Multi-Objective Evolutionary Algorithm. To this end, a fitness function is introduced to measure three different objectives based on the Complexity Profile model. Our hypothesis is that the Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance may be used in a wider set of case-bases, achieving a good balance between the reduction of cases and the problem-solving ability of the CaseBased Reasoning system. Finally, from a set of the experiments, our proposed Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance shows regularly good results with different sets of case-bases with different proportion of redundant and noisy cases.Eduardo Lupiani · Jose M. Juarez · Jose Palma University of Murcia, Spain E-mail: {elupiani,jmjuarez,jtpalma}@um.es Susan Craw · Stewart Massie Robert Gordon University, Scotland, UK E-mail: {s.craw,s.massie}@rgu.ac.uk 2 E. Lupiani et al.
Knowledge-based decision support systems (KBDSS) hold up business and organizational decision-making activities on the basis of the knowledge available concerning the domain under question. One of the main problems with knowledge bases is that their construction is a time-consuming task. A number of methodologies have been proposed in the context of the Semantic Web to assist in the development of ontology-based knowledge bases. In this paper, we present a technique for populating knowledge bases from semi-structured text which take advantage of the semantic underpinnings provided by ontologies. This technique has been tested and evaluated in the financial domain
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