Building Compact Competent Case-Bases

Smyth, Barry; McKenna, Elizabeth

doi:10.1007/3-540-48508-2_24

Cited by 83 publications

(66 citation statements)

References 11 publications

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“…Within the CBR community and the machine learning community studying instance-based learning, extensive effort has been devoted to addressing the swamping utility problem for case retrieval with case-base maintenance methods for controlling case-base growth, with the goal of generating case bases that are compact but retain coverage of as many problems as possible. Methods for developing compact competent case bases include selective deletion (e.g., [7,18]), selective case retention (e.g., [19][20][21]), and competence-aware construction of case bases [8,[22][23][24][25]. Such methods generally trade off size against accuracy; they aim to retain as much competence as possible for a given amount of compression.…”

Section: Scaling Cbr To Big Datamentioning

confidence: 99%

“…The case-based reasoning community has long been aware of the challenges of scaling up CBR to large case bases. The primary response has been case-base maintenance methods aimed at reducing the size of the case base while preserving competence (e.g., [7,8]). Such methods have proven effective at making good use of case knowledge within storage limits.…”

Section: Introductionmentioning

confidence: 99%

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CBR Meets Big Data: A Case Study of Large-Scale Adaptation Rule Generation

Jalali

Leake

2015

Case-Based Reasoning Research and Development

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Abstract. Adaptation knowledge generation is a difficult problem for CBR. In previous work we developed ensembles of adaptation for regression (EAR), a family of methods for generating and applying ensembles of adaptation rules for case-based regression. EAR has been shown to provide good performance, but at the cost of high computational complexity. When efficiency problems result from case base growth, a common CBR approach is to focus on case base maintenance, to compress the case base. This paper presents a case study of an alternative approach, harnessing big data methods, specifically MapReduce and locality sensitive hashing (LSH), to make the EAR approach feasible for large case bases without compression. Experimental results show that the new method, BEAR, substantially increases accuracy compared to a baseline big data k-NN method using LSH. BEAR's accuracy is comparable to that of traditional k-NN without using LSH, while its processing time remains reasonable for a case base of millions of cases. We suggest that increased use of big data methods in CBR has the potential for a departure from compression-based case-base maintenance methods, with their concomitant solution quality penalty, to enable the benefits of full case bases at much larger scales.

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Section: Scaling Cbr To Big Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CBR Meets Big Data: A Case Study of Large-Scale Adaptation Rule Generation

Jalali

Leake

2015

Case-Based Reasoning Research and Development

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“…For example, as an alternative to case deletion, Smyth & McKenna (1999b) use their competence model to develop a competence-guided case addition algorithm. In related work, Zhu & Yang (1999) describe a case addition algorithm that has the added advantage of providing a guaranteed lower bound on resulting competence.…”

Section: Harmful Cases Competence Models and Selective Retentionmentioning

confidence: 99%

Retrieval, reuse, revision and retention in case-based reasoning

Mántaras

McSherry

Bridge

et al. 2005

The Knowledge Engineering Review

Self Cite

444

185

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Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention. R. LÓPEZ DE MÁNTARAS ET AL.

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“…Exemplar learning (also known as case-based reasoning, Smyth and McKenna 1999;Zhu and Yang 1999) is a combinatorial optimization problem similar to that of feature selection (Kwak and Choi 2002). Due to the high complexity of the optimization procedure involved, it is often preferred to use randomized (Pkalska et al 2006) or greedy methods (Battiti 1994;Wilson and Martinez 2000).…”

Section: Exemplar Learning On Embedded Devicesmentioning

confidence: 99%

Incremental exemplar learning schemes for classification on embedded devices

Jain

Nikovski

2008

Mach Learn

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Although memory-based classifiers offer robust classification performance, their widespread usage on embedded devices is hindered due to the device's limited memory resources. Moreover, embedded devices often operate in an environment where data exhibits evolutionary changes which entails frequent update of the in-memory training data. A viable option for dealing with the memory constraint is to use Exemplar Learning (EL) schemes that learn a small memory set (called the exemplar set) of high functional information that fits in memory. However, traditional EL schemes have several drawbacks that make them inapplicable for embedded devices; (1) they have high memory overheads and are unable to handle incremental updates to the exemplar set, (2) they cannot be customized to obtain exemplar sets of any user-defined size that fits in the memory and (3) they learn exemplar sets based on local neighborhood structures that do not offer robust classification performance. In this paper, we propose two novel EL schemes, EBEL (Entropy-Based Exemplar Learning) and ABEL (AUC-Based Exemplar Learning) that overcome the aforementioned short-comings of traditional EL algorithms. We show that our schemes efficiently incorporate new training datasets while maintaining high quality exemplar sets of any user-defined size. We present a comprehensive experimental analysis showing excellent classificationaccuracy versus memory-usage tradeoffs using our proposed methods.

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Building Compact Competent Case-Bases

Cited by 83 publications

References 11 publications

CBR Meets Big Data: A Case Study of Large-Scale Adaptation Rule Generation

CBR Meets Big Data: A Case Study of Large-Scale Adaptation Rule Generation

Retrieval, reuse, revision and retention in case-based reasoning

Incremental exemplar learning schemes for classification on embedded devices

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