A Knowledge-Light Approach to Regression Using Case-Based Reasoning

McDonnell, Neil; Cunningham, Pádraig

doi:10.1007/11805816_9

Cited by 14 publications

(4 citation statements)

References 56 publications

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“…This is based on learning adaptation knowledge by understanding relationships among cases; e.g. ensemble learning for adaptation [19], or gradient learning for adaptation [20].…”

Section: Cognition From Self-reflectionmentioning

confidence: 99%

Case Based Reasoning as a Model for Cognitive Artificial Intelligence

Craw

Aamodt

2018

Case-Based Reasoning Research and Development

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Cognitive Systems understand the world through learning and experience. Case Based Reasoning (CBR) systems naturally capture knowledge as experiences in memory and they are able to learn new experiences to retain in their memory. CBR's retrieve and reuse reasoning is also knowledge-rich because of its nearest neighbour retrieval and analogy-based adaptation of retrieved solutions. CBR is particularly suited to domains where there is no well-defined theory, because they have a memory of experiences of what happened, rather than why/how it happened. CBR's assumption that 'similar problems have similar solutions' enables it to understand the contexts for its experiences and the 'bigger picture' from clusters of cases, but also where its similarity assumption is challenged. Here we explore cognition and meta-cognition for CBR through self-reflection and introspection of both memory and retrieve and reuse reasoning. Our idea is to embed and exploit cognitive functionality such as insight, intuition and curiosity within CBR to drive robust, and even explainable, intelligence that will achieve problemsolving in challenging, complex, dynamic domains.

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“…This is based on learning adaptation knowledge by understanding relationships among cases; e.g. ensemble learning for adaptation [19], or gradient learning for adaptation [20].…”

Section: Cognition From Self-reflectionmentioning

confidence: 99%

Case Based Reasoning as a Model for Cognitive Artificial Intelligence

Craw

Aamodt

2018

Case-Based Reasoning Research and Development

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“…McDonnell and Cunningham 46 proposed four different types of knowledge-light adaption methods applicable to nonlinear problems. In our work, a general-purpose nonlinear adaptation method which combines case differences vector and case gradient vector is used.…”

Section: Iiic Adaption Methods Which Combines Case Differences and Gmentioning

confidence: 99%

A self‐adaptive case‐based reasoning system for dose planning in prostate cancer radiotherapy

Petrović

Sundar

2011

Medical Physics

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“…Our basic approach to adaptation rule generation builds on the case difference heuristic approach proposed by Hanney and Keane [3] and further explored by others (e.g., [4,5]). The case difference approach builds new adaptation rules from pairs of cases and compares their problem parts (respectively, solution parts), and identifies their differences to generate a candidate rule mapping the observed difference in problems to the observed difference in solutions.…”

Section: Learning and Applying Ensembles Of Adaptation Rulesmentioning

confidence: 99%

“…McDonnell and Cunningham [5] refine the case difference heuristic to address two problems. The first is that the effect of variations in feature values on the solution may differ according to the feature considered.…”

Section: Learning Adaptations From the Case Basementioning

confidence: 99%

Extending Case Adaptation with Automatically-Generated Ensembles of Adaptation Rules

Jalali

Leake

2013

Case-Based Reasoning Research and Development

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Abstract. Case-based regression often relies on simple case adaptation methods. This paper investigates new approaches to enriching the adaptation capabilities of case-based regression systems, based on the use of ensembles of adaptation rules generated from the case base. The paper explores both local and global methods for generating adaptation rules from the case base, and presents methods for ranking the generated rules and combining the resulting ensemble of adaptation rules to generate new solutions. It tests these methods in five standard domains, evaluating their performance compared to four baseline methods, standard k-NN, linear regression, locally weighted linear regression, and an ensemble of k-NN predictors with different feature subsets. The results demonstrate that the proposed method generally outperforms the baselines and that the accuracy of adaptation based on locally-generated rules is highly competitive with that of global rule-generation methods with much greater computational cost.

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A Knowledge-Light Approach to Regression Using Case-Based Reasoning

Cited by 14 publications

References 56 publications

Case Based Reasoning as a Model for Cognitive Artificial Intelligence

Case Based Reasoning as a Model for Cognitive Artificial Intelligence

A self‐adaptive case‐based reasoning system for dose planning in prostate cancer radiotherapy

Extending Case Adaptation with Automatically-Generated Ensembles of Adaptation Rules

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