Titlebook: Case-Based Reasoning Research and Development; 30th International C Mark T. Keane,Nirmalie Wiratunga Conference proceedings 2022 The Editor

Ingenuity

Theoretical and?Experimental Study of?a?Complexity Measure for?Analogical Transfer At an experimental level, it studies the correlation of the complexity measure with the accuracy of the conducted label inference, as well as with the classification task difficulty, as captured by the class overlapping degree.

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W. Mestwerdt,O. Müller,H. Brandaurning distorts the case-retrieval process. We find that bias is addressed with a minimum impact on case-based predictions - little more than the predictions that need to be changed are changed. However, the effect on explanation is more significant as the case-retrieval . is impacted.

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Anticoagulants

Francis I. Onuska,Francis W. KarasekThe highly accurate effort estimates resulting from a couple of experiments on real data show that estimation by analogy presents with the aid of an automated environment an eminently practical technique. As a consequence, this approach impacts estimation accuracy and thus success of industrial software development projects.

DEVIL

A Deep Learning Approach to?Solving Morphological Analogiesemonstrate that ANNr outperforms the state of the art on 11 languages. We analyze ANNr results for Navajo and Georgian, languages on which the model performs worst and best, to explore potential correlations between the mistakes of ANNr and linguistic properties.

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杠桿支點

A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanatito multi-class settings, or are limited in their ability to generate alternative counterfactuals. This paper extends recent case-based approaches by presenting a novel, general-purpose, case-based solution for counterfactual generation to address these shortcomings. We report a series of experiments

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murmur

“Better” Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfaunterfactuals relative to baseline methods on coverage and distances metrics. This is the first counterfactual method specifically designed to meet identified psychological requirements of end-users, rather than merely reflecting the intuitions of algorithm designers.