A new strategy for case-based reasoning retrieval using classification based on association

This paper proposes a novel strategy, Case-Based Reasoning Using Association Rules (CBRAR) to improve the performance of the Similarity base Retrieval SBR, classed frequent pattern trees FP-CAR algorithm, in order to disambiguate wrongly retrieved cases in Case-Based Reasoning (CBR). CBRAR use class as-sociation rules (CARs) to generate an optimum FP-tree which holds a value of each node. The possible advantage offered is that more efficient results can be gained when SBR returns uncertain answers. We compare the CBR Query as a pattern with FP-CAR patterns to identify the longest length of the voted class. If the patterns are matched, the proposed strategy can select not just the most similar case but the correct one. Our experimental evaluation on real data from the UCI repository indicates that the proposed CBRAR is a better approach when com-pared to the accuracy of the CBR systems used in our experiments

A new strategy for case-based reasoning retrieval using classification based on association

Cased Based Reasoning (CBR) is an important area of research in the field of Artificial Intelli-gence. It aims to solve new problems by adapting solutions, that were used to solve previous similar ones. Among the four typical phases - retrieval, reuse, revise and retain, retrieval is a key phase in CBR approach, as the retrieval of wrong cases can lead to wrong decisions. To ac-complish the retrieval process, a CBR system exploits Similarity-Based Retrieval (SBR). How-ever, SBR tends to depend strongly on similarity knowledge, ignoring other forms of knowledge, that can further improve retrieval performance.The aim of this study is to integrate class association rules (CARs) as a special case of associa-tion rules (ARs), to discover a set (of rules) that can form an accurate classifier in a database. It is an efficient method when used to build a classifier, where the target is pre-determined. The proposition for this research is to answer the question of whether CARs can be integrated into a CBR system. A new strategy is proposed that suggests and uses mining class association rules from previous cases, which could strengthen similarity based retrieval (SBR). The propo-sition question can be answered by adapting the pattern of CARs, to be compared with the end of the Retrieval phase. Previous experiments and their results to date, show a link between CARs and CBR cases. This link has been developed to achieve the aim and objectives.A novel strategy, Case-Based Reasoning using Association Rules (CBRAR) is proposed to improve the performance of the SBR and to disambiguate wrongly retrieved cases in CBR. CBRAR uses CARs to generate an optimum frequent pattern tree (FP-tree) which holds a val-ue of each node. The possible advantage offered is that more efficient results can be gained, when SBR returns uncertain answers. In addition, CBRAR has been evaluated using two sources of CBR frameworks - Jcolibri and Free CBR. With the experimental evaluation on real datasets indicating that the proposed CBRAR is a better approach when compared to CBR systems, offering higher accuracy and lower error rate

Proceedings of the CSE 2017 Annual PGR Symposium (CSE-PGSym17)

Welcome to the Proceedings of the second Annual Postgraduate Research Symposium of the School of Computing, Science and Engineering (CSE-PGSym 2017). After the success of the first symposium, the school is delighted to run its second symposium which is being held in The Old Fire Station on 17th March 2017. The symposium is organised by the Salford Innovation Research Centre (SIRC) to provide a forum for the PGR community in the school to share their research work, engage with their peers and staff and stimulate new ideas. In line with SIRC’s strategy, the symposium aims to bring together researchers from the six groups that make up the centre to engage in multidisciplinary discussions and collaborations. It also aims to contribute to the creation of a collaborative environment within the Research Centre and the Groups and share information and explore new ideas. This is also aligned with the University’s ICZ (Industrial Collaboration Zone) programme for creating cultural, physical and virtual environments for collaboration, innovation and learning

Enhancing case-based reasoning retrieval using classification based on associations

The aim of this paper was to perform an extension of (CBRAR) strategy, Case-Based Reasoning using Association Rules to enhance the performance of the Similarity Base Retrieval SBR. FP-CAR classed frequent pattern tree algorithms are used in order to select correctly retrieved cases in Case-Based Reasoning (CBR). Class Association Rules (CARs) are utilized to generate an optimum FP-tree which holds a value of each node. The potential benefit offered is that more efficient results can be obtained when the retrieval phase returns uncertain answers. A comparison of CBR Query with FP-CAR is performed as patterns in order to identify the longest length nodes of the voted class. If the patterns are matched, the proposed CBRAR can choose the most similar and correct case. The experimental evaluation on a real dataset shows that the proposed CBRAR is a superior approach when likened to the accuracy of the CBR systems used in this investigation