The Bees algorithm to extract fuzzy measures for sample data

Wang, Xiaojing; Cummins, Jeremy; Ceberio, Martine

doi:10.1109/nafips.2011.5751948

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…The Bees algorithm, proposed in [16], was also used for FME in [25]. It uses bees' natural food foraging habits as a model for the exploration of the search space.…”

Section: B Optimization Techniques Used For Fmementioning

confidence: 99%

“…This problem is therefore tackled as an optimization problem. Several optimization approaches have been used to extract fuzzy measures from sample data, such as gradient descent algorithms [6], genetic algorithms [4], [24], [26], and the Bees algorithm [25]. More specifically, fuzzy measure extraction constitutes a constrained optimization problem since the optimal solution must also satisfy the monotonicity constraints inherent to the fuzzy measure we aim at determining.…”

Section: Introductionmentioning

confidence: 99%

“…The Bees algorithm has been successfully used in many optimization problems but requires a significant amount of tuning to attain reasonable performance. We adjusted it to the needs of fuzzy measure extraction problems: in [25], we showed that using the Bees algorithm to extract fuzzy measures from sample data provides better performance with results of similar or better accuracy than existing approaches. In this article, we use an interval constraint solver to shrink the search space to focus the Bees search to improve the overall performance and provide a certificate that we explore the right parts of the search space.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Interval-based algorithms to extract fuzzy measures for Software Quality Assessment

Wang

Contreras

Ceberio

et al. 2012

2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)

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In this paper, we consider the problem of automatically assessing sofware quality. We show that we can look at this problem, called Software Quality Assessment (SQA), as a multicriteria decision-making problem. Indeed, just like software is assessed along different criteria, Multi-Criteria Decision Making (MCDM) is about decisions that are based on several criteria that are usually conflicting and non-homogenously satisfied. Nonadditive (fuzzy) measures along with the Choquet integral can be used to model and aggregate the levels of satisfaction of these criteria by considering their relationships. However, in practice, fuzzy measures are difficult to identify. An automated process is necessary and possible when sample data is available. Several optimization approaches have been proposed to extract fuzzy measures from sample data; e.g., genetic algorithms, gradient descent algorithms, and the Bees algorithm, all local search techniques. In this article, we propose a hybrid approach, combining the Bees algorithm and an interval constraint solver, resulting in a focused search expected to be less prone to falling into local results. Our approach, when tested on SQA decision data, shows promise and compares well to previous approaches to SQA that were using machine learning techniques.

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“…The Bees algorithm, proposed in [16], was also used for FME in [25]. It uses bees' natural food foraging habits as a model for the exploration of the search space.…”

Section: B Optimization Techniques Used For Fmementioning

confidence: 99%