Complexity Profiling for Informed Case-Base Editing

Massie, Stewart; Craw, Susan; Wiratunga, Nirmalie

doi:10.1007/11805816_25

Cited by 15 publications

(13 citation statements)

References 14 publications

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“…We based both of them on Massie et al's case complexity measure [12]. But, when we computed these measures pre-and post-maintenance, we found, overwhelmingly, that they disagreed with the other measures about how complexity had changed, and so we exclude them here.…”

Section: Measures Of Overlap Of Attribute Valuesmentioning

confidence: 98%

“…F 2 . In [2], we also describe 4 additional measures: one is from the Case-Based Reasoning (CBR) literature [7]; we define the other 3 from ideas presented in the CBR literature [12,16]. Table 2 [9,10], and we have also shown these in the Table. For the purposes of this paper, we select just three of the complexity measures, one from each of the categories.…”

Section: Measures For Maintenancementioning

confidence: 99%

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On Dataset Complexity for Case Base Maintenance

Cummins

Bridge

2011

Case-Based Reasoning Research and Development

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Abstract.We present what is, to the best of our knowledge, the first analysis that uses dataset complexity measures to evaluate case base editing algorithms. We select three different complexity measures and use them to evaluate eight case base editing algorithms. While we might expect the complexity of a case base to decrease, or stay the same, and the classification accuracy to increase, or stay the same, after maintenance, we find many counter-examples. In particular, we find that the RENN noise reduction algorithm may be over-simplifying class boundaries.

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Section: Measures Of Overlap Of Attribute Valuesmentioning

confidence: 98%

Section: Measures For Maintenancementioning

confidence: 99%

On Dataset Complexity for Case Base Maintenance

Cummins

Bridge

2011

Case-Based Reasoning Research and Development

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“…There are a wealth of approaches to perform CBM, such as those published in (Brighton and Mellish (1999); Gates (1972); Massie et al (2006); Pan et al (2007); Smyth and Keane (1995); Wilson (1972); Wilson and Martinez (2000)). These algorithms usually try to classify the cases within the case-base as redundant or noisy, and delete them according to a specific deletion policy.…”

Section: Case-base Maintenancementioning

confidence: 99%

“…Nonetheless, the maintenance domain is a weak theory domain and it is difficult to determine what is the ideal or optimum number of cases to achieve the best results (Leake and Wilson (2011)). For this reason, the CBM algorithms use heuristics to select those cases that will be part of the maintained case-base, where these heuristics are designed to remove (or select) either redundant or noisy cases (Massie et al (2005(Massie et al ( , 2006). Therefore, whereas some more aggressive CBM algorithms try to find case-bases within or close to the knee bend area, other more conservative CBM algorithms search for a case-base within the stable error region.…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of this CBM is to generate a case-base with low number of redundant cases and few noisy cases, minimizing three objectives based on Complexity Profiling (Massie et al (2005(Massie et al ( , 2006). In the last decades, Multi-Objective Evolutionary Algorithms (MOEAs) have been applied successfully in multi-objective optimization problems (Coello et al (2007); Eiben and Smith (2003); Yu and Gen (2010)).…”

Section: Introductionmentioning

confidence: 99%

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Case-base maintenance with multi-objective evolutionary algorithms

et al. 2015

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CopyrightItems in 'OpenAIR@RGU', Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in 'OpenAIR@RGU' infringes copyright, please contact openair-help@rgu.ac.uk with details. The item will be removed from the repository while the claim is investigated.The final publication is available at Springer via http://dx.doi.org/10.1007/s10844-015-0378-z Case-Base Maintenance with Multi-Objective Evolutionary AlgorithmsAbstract Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solving ability of the Case-Base Reasoning system. However, when the case-base has many cases, then performance problems arise due to the time needed to find those similar cases to the input problem. At this point, Case-Base Maintenance algorithms can be used to reduce the number of cases and maintain the accuracy of the Case-Base Reasoning system at the same time. Whereas Case-Base Maintenance algorithms typically use a particular heuristic to remove (or select) cases from the case-base, the resulting maintained case-base relies on the proportion of redundant and noisy cases that are present in the case-base, among other factors. That is, a particular Case-Base Maintenance algorithm is suitable for certain types of case-bases that share some indicators, such as redundancy and noise levels.In the present work, we consider Case-Base Maintenance as a multi-objective optimization problem, which is solved with a Multi-Objective Evolutionary Algorithm. To this end, a fitness function is introduced to measure three different objectives based on the Complexity Profile model. Our hypothesis is that the Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance may be used in a wider set of case-bases, achieving a good balance between the reduction of cases and the problem-solving ability of the CaseBased Reasoning system. Finally, from a set of the experiments, our proposed Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance shows regularly good results with different sets of case-bases with different proportion of redundant and noisy cases.Eduardo Lupiani · Jose M. Juarez · Jose Palma University of Murcia, Spain E-mail: {elupiani,jmjuarez,jtpalma}@um.es Susan Craw · Stewart Massie Robert Gordon University, Scotland, UK E-mail: {s.craw,s.massie}@rgu.ac.uk 2 E. Lupiani et al.

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Choosing a Case Base Maintenance Algorithm using a Meta-Case Base

Cummins

Bridge

2011

Research and Development in Intelligent Systems XXVIII

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In Case-Based Reasoning (CBR), case base maintenance algorithms remove noisy or redundant cases from case bases. The best maintenance algorithm to use on a particular case base at a particular stage in a CBR system's lifetime will vary. In this paper, we propose a meta-case-based classifier for selecting the best maintenance algorithm. The classifier takes in a description of a case base that is to undergo maintenance, and uses meta-cases-descriptions of case bases that have undergone maintenance-to predict the best maintenance algorithm. For describing case bases, we use measures of dataset complexity. We present the results of experiments that show the classifier can come close to selecting the best possible maintenance algorithms.

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Complexity Profiling for Informed Case-Base Editing

Cited by 15 publications

References 14 publications

On Dataset Complexity for Case Base Maintenance

On Dataset Complexity for Case Base Maintenance

Case-base maintenance with multi-objective evolutionary algorithms

Choosing a Case Base Maintenance Algorithm using a Meta-Case Base

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