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

A comparison of case-based reasoning and regression analysis approaches for cost uncertainty modeling

This thesis presents case-based reasoning approach for estimating the cost and modeling cost uncertainty of a new product in the concept selection stage. Case-based reasoning (CBR) is an approach which uses old cases/experiences to understand and solve new problems. The CBR approach consists of creating a knowledge-base (or database) containing past cases (products), defining a new case (concept), retrieving cases similar to the new case, and adjusting the solution of the retrieved cases to the new case. The first paper compares case-based reasoning, in studying the effects of varying design attribute specifications on cost estimation accuracy and cost distribution reliability. Case-based reasoning with cost estimation is compared with three methods: analogy-based cost estimation, case-based reasoning without cost adjustment, and regression analysis. Four automobile concepts with similar performance attribute specifications but varying design attribute specifications are defined and the comparison is made using leave-one-out cross-validation technique to a knowledge-base of 345 automobiles. The second paper further establishes case-based reasoning with cost adjustment by studying the optimum number of design attributes for specifying a concept. The results show that case-based reasoning and with cost adjustment performed best for cost estimation accuracy and cost distribution reliability when one design attribute is specified for the concept in addition to performance attributes --Abstract, page iv

Textual Case-based Reasoning for Spam Filtering: a Comparison of Feature-based and Feature-free Approaches

Spam filtering is a text classification task to which Case-Based Reasoning (CBR) has been successfully applied. We describe the ECUE system, which classifies emails using a feature-based form of textual CBR. Then, we describe an alternative way to compute the distances between cases in a feature-free fashion, using a distance measure based on text compression. This distance measure has the advantages of having no set-up costs and being resilient to concept drift. We report an empirical comparison, which shows the feature-free approach to be more accurate than the feature-based system. These results are fairly robust over different compression algorithms in that we find that the accuracy when using a Lempel-Ziv compressor (GZip) is approximately the same as when using a statistical compressor (PPM). We note, however, that the feature-free systems take much longer to classify emails than the feature-based system. Improvements in the classification time of both kinds of systems can be obtained by applying case base editing algorithms, which aim to remove noisy and redundant cases from a case base while maintaining, or even improving, generalisation accuracy. We report empirical results using the Competence-Based Editing (CBE) technique. We show that CBE removes more cases when we use the distance measure based on text compression (without significant changes in generalisation accuracy) than it does when we use the feature-based approach

Resources and users in the tagging process: approaches and case studies

In this contribution we propose a comparison between two distinct approaches to the annotation of digital resources. The former, top-down, is rooted in the cathedral model and is based on an authoritative, centralized definition of the adopted mark-up language; the latter, bottom-up, refers to the bazaar model and is based on the contributions of a community of users. These two approaches are analyzed taking into account both their descriptive potential and the constraints they impose on the reasoning process of recommender systems, with special reference to user profiling. Three case studies are described, with reference to research projects that apply these approaches in the contexts of e-learning and knowledge management

Service-Learning and Case-Based Learning’s Impact on Student’s Clinical Reasoning: A Repeated Measures Design Study

Clinical reasoning is crucial for the occupational therapy profession to thrive in an ever-changing healthcare environment but is seldom isolated for explicit instruction and outcome measurement in educational course curricula. A single-factor repeated measures design study was conducted to compare the impact of didactic case-based learning and experiential service-learning on the development of the clinical reasoning of students at a midwestern public university’s entry-level Master of Occupational Therapy program. The participants were sixteen graduate occupational therapy students who had completed their foundation-level courses. Participants explored modes of clinical reasoning in occupational therapy for eight weeks (the first half of the semester), using didactic case-based learning, and then participated in an eight-week (the second half of the semester), experiential service-learning practicum engaging uninsured and underinsured adult clients in occupational therapy evaluation and intervention. The dependent variable of clinical reasoning was measured using the Self-Assessment of Clinical Reflection and Reasoning (SACRR) survey which was administered at the start and end of both phases of the study. SACCR scores generally increased and were significant during the experiential phase (MD =7.384, t (12) = 2.27, p = .042, d = 0.63, 95% CI [0.02, 1.22]) An analysis of changes in individual SACRR items provided insights into the development of clinical reasoning modes of practice in novice clinicians. The comparison of didactic case-based learning and experiential service-learning supports the use of either or both approaches. The sequence and weightage of each strategy could be individually adjusted in course syllabi and curricula to fit student learning needs

Admissibility of forensic voice comparison testimony in England and Wales

In 2015 the Criminal Practice Directions (CPD) on admissibility of expert evidence in England and Wales were revised. They emphasised the principle that “the court must be satisfied that there is a sufficiently reliable scientific basis for the evidence to be admitted”. The present paper aims to assist courts in understanding from a scientific perspective what would be necessary to demonstrate the validity of testimony based on forensic voice comparison. We describe different technical approaches to forensic voice comparison that have been used in the UK, and critically review the case law on their admissibility. We conclude that courts have been inconsistent in their reasoning. In line with the CPD, we recommend that courts enquire as to whether forensic practitioners have made use of data and analytical methods that are appropriate and adequate for the case under consideration, and that courts require forensic practitioners to empirically demonstrate the level of performance of their forensic voice comparison system under conditions reflecting those of the case under consideration

Exploring the potential of defeasible argumentation for quantitative inferences in real-world contexts: An assessment of computational trust

Argumentation has recently shown appealing properties for inference under uncertainty and conflicting knowledge. However, there is a lack of studies focused on the examination of its capacity of exploiting real-world knowledge bases for performing quantitative, case-by-case inferences. This study performs an analysis of the inferential capacity of a set of argument-based models, designed by a human reasoner, for the problem of trust assessment. Precisely, these models are exploited using data from Wikipedia, and are aimed at inferring the trustworthiness of its editors. A comparison against non-deductive approaches revealed that these models were superior according to values inferred to recognised trustworthy editors. This research contributes to the field of argumentation by employing a replicable modular design which is suitable for modelling reasoning under uncertainty applied to distinct real-world domains

An explainable approach to deducing outcomes in european court of human rights cases using ADFs

In this paper we present an argumentation-based approach to representing and reasoning about a domain of law that has previously been addressed through a machine learning approach. The domain concerns cases that all fall within the remit of a specific Article within the European Court of Human Rights. We perform a comparison between the approaches, based on two criteria: ability of the model to accurately replicate the decision that was made in the real life legal cases within the particular domain, and the quality of the explanation provided by the models. Our initial results show that the system based on the argumentation approach improves on the machine learning results in terms of accuracy, and can explain its outcomes in terms of the issue on which the case turned, and the factors that were crucial in arriving at the conclusion