The Effectiveness of Case-Based Reasoning: An Application in Sales Promotions

This paper deals with Case-based Reasoning (CBR) as a support technology for sales promotion (SP) decisions. CBR-systems try to mimic analogical reasoning, a form of human reasoning that is likely to occur in weakly-structured problem solving, such as the design of sales promotions. In an empirical study, we find evidence that use of the CBR-system improves the quality of SP-campaign proposals. In terms of the creativity of the proposals, decision-makers who think highly divergent (i.e., who tend to generate many, and diverse ideas in response to a problem) benefit most from prolonged system usage. Creativity, in turn, is positively related to the (practical) usability of a proposal. These results suggest that the CBR-system is most effective when it is used as an idea-generation tool that reinforces the strength of divergent (creative) thinkers. A convergent thinking style, in which case the CBR-system has a compensating role, even has a negative impact on CBR-system usage. Increasing the decision-maker's personal belief in the usefulness of the system, e.g., by training or education, may help to alleviate this reluctance to use the CBR-system.marketing management support systems;sales promotions;case-based reasoning;weakly-structured decision making

Determinants of Sovereign Ratings: A Comparison of Case-Based Reasoning and Ordered Probit Approaches

The paper compares two alternative techniques for the modelling of the determinants of sovereign ratings, specifically, ordered probit and case-based reasoning. Despite the differences in approach the two alternative modelling approaches produce similar results in terms of which variables are significant and forecast accuracy. This suggests that either approach can be used, and that there is some robustness in the results. As regards significant variables, both models find that a proxy for technological development, specifically, mobile phone use, is the most important variable. Apart from the technology proxy, a range of conventional macroeconomic variables are found to be significant, in particular GDP and inflation. The models are then used to produce forecasts for 2002 and for a set of unrated countries. The forecast comparison indicates the critical role played by the technology proxy variable in the modelling.Sovereign Ratings, Ordered Response Models, Case-Based Reasoning

Towards a Cloud Architectural Decision Framework using Case-Based Reasoning and Rule-Based Reasoning

Correct decision making about the cloud platform architecture is crucial for the success of any cloud migration project; bad decisions can lead to undesirable consequences. Rules Based Reasoning (RBR), a popular approach for solving clearly defined problems, can be used for cloud platform recommendation if a comprehensive set of requirements are available. However, the responsibility of decision-making is increasingly moving away from the hands of the technical subject matter experts, and into the hands of the business sponsors who, despite being the end-all, be-all decision-makers, typically do not have access to sufficient information at the initial stages of the project lifecycle. Therefore, in this paper, we propose combining Case Based Reasoning (CBR) with RBR to assist business sponsors in making strategic decisions between public, private and hybrid cloud with a high level of confidence even at the initial stages of the project

Towards Case Completion with inferencing and solution identification using ‘Nested CBR’

Case Based Reasoning (CBR) provides a framework to capture past problems and their solutions to solve future problems. Problem cases are typically complete; however, it is not always possible to have a complete problem case due to complexity, lack of data, or availability of human expertise. The limitations of existing approaches for handling incomplete cases include a reliance upon manual input, such as Conversational CBR (CCBR) and Incremental CBR (ICBR), or a rigid structure of relationships maintained using a semantic ontology, to infer the missing feature values. Using the case base to infer feature values increases the efficiency and likelihood of identifying a relevant solution compared with manual interactions because the case base is based upon proven problem to solution correlation. Therefore, in this work-in-progress paper, we propose \u27Nested CBR\u27 as an approach for the automated completion of partial problem cases, and the subsequent solution identification, thereby avoiding manual input and improving solution efficiency and meaning

Multiway principal component analysis and case base reasoning methodology for abnormal situation detection in a nutrient removing SBR

Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) approaches are applied in a biological nutrient removal process. The goal is monitoring of normal and abnormal operation conditions in this process. MPCA is used as a compression tool where with few variables the process can be described, as well as, to detect batches with abnormal conditions. However, some abnormal conditions (alarms) are omitted or, otherwise some false alarms are produced. Then, CBR is proposed. It assumes that similar problems should have similar solutions. In biological process, these problems generally are operational situations under normal or abnormal situations: low ORP, high pH, sensors fault, among others. These symptoms can be stored in a Case Base (CB) in order to diagnose future situations. Several proof are made in order to find the better methodology.Postprint (author’s final draft

Comparing Fusion Techniques for the ImageCLEF 2013 Medical Case Retrieval Task

Retrieval systems can supply similar cases with a proven diagnosis to a new example case under observation to help clinicians during their work. The ImageCLEFmed evaluation campaign proposes a framework where research groups can compare case-based retrieval approaches. This paper focuses on the case-based task and adds results of the compound figure separation and modality classification tasks. Several fusion approaches are compared to identify the approaches best adapted to the heterogeneous data of the task. Fusion of visual and textual features is analyzed, demonstrating that the selection of the fusion strategy can improve the best performance on the case-based retrieval task

The Effectiveness of Case-Based Reasoning: An Application in Sales Promotions

This paper deals with Case-based Reasoning (CBR) as a support technology for sales promotion (SP) decisions. CBR-systems try to mimic analogical reasoning, a form of human reasoning that is likely to occur in weakly-structured problem solving, such as the design of sales promotions. In an empirical study, we find evidence that use of the CBR-system improves the quality of SP-campaign proposals. In terms of the creativity of the proposals, decision-makers who think highly divergent (i.e., who tend to generate many, and diverse ideas in response to a problem) benefit most from prolonged system usage. Creativity, in turn, is positively related to the (practical) usability of a proposal. These results suggest that the CBR-system is most effective when it is used as an idea-generation tool that reinforces the strength of divergent (creative) thinkers. A convergent thinking style, in which case the CBR-system has a compensating role, even has a negative impact on CBR-system usage. Increasing the decision-maker's personal belief in the usefulness of the system, e.g., by training or education, may help to alleviate this reluctance to use the CBR-system

Fusion Techniques in Biomedical Information Retrieval

For difficult cases clinicians usually use their experience and also the information found in textbooks to determine a diagnosis. Computer tools can help them supply the relevant information now that much medical knowledge is available in digital form. A biomedical search system such as developed in the Khresmoi project (that this chapter partially reuses) has the goal to fulfil information needs of physicians. This chapter concentrates on information needs for medical cases that contain a large variety of data, from free text, structured data to images. Fusion techniques will be compared to combine the various information sources to supply cases similar to an example case given. This can supply physicians with answers to problems similar to the one they are analyzing and can help in diagnosis and treatment planning