Evolutionary dataset optimisation: learning algorithm quality through evolution

Wilde, Henry; Knight, Vincent A.; Gillard, Jonathan

doi:10.1007/s10489-019-01592-4

Cited by 8 publications

(2 citation statements)

References 36 publications

(18 reference statements)

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“…The value of 0 implies no distinct association, and the value of −1 indicates the wrong assignment to a cluster [ 36 ]. Silhouette coefficient is employed by scholars widely to calculate the optimal number of clusters [ 37 , 38 ]. Let us assume that A and C are two different clusters and i ′ ∈ A .…”

Section: Preliminariesmentioning

confidence: 99%

Large-scale group decision-making (LSGDM) for performance measurement of healthcare construction projects: Ordinal Priority Approach

et al. 2022

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People with various skill sets and backgrounds are usually found working on projects and thus, group decision-making (GDM) is one of the most important functions within any project. However, when projects concern healthcare or other critical services for proletariat or general public (especially during COVID19), the importance of GDM can hardly be overstated. Measuring the performance of healthcare construction projects is a critical activity and should be gauged based on the input from a large number of stakeholders. Such problems are usually recognized as large-scale group decision-making (LSGDM). In the current study, we aim to propose a decision support system for measuring the performance of healthcare construction projects against a large number of experts using ordinal data. The study identifies several key indicators from literature and recorded the observations of a large number of experts about these indicators. After that, the acceptable range of complexity is specified, the Silhouette plot is provided to find the optimal number of clusters, and the ordinal K-means method is employed to cluster the experts’ opinions. Later, the confidence level is measured using a novel Weighted Kendall’s W for the optimal number of the clusters, and the threshold is checked. Finally, the conventional problem is solved using the Group Weighted Ordinal Priority Approach (GWOPA) model in multiple attributes decision making (MADM), and the performance of the projects is determined. The validity of the proposed approach is confirmed through a comparative analysis. Also, a real-world case is solved, and the performance of some healthcare construction projects in China is gauged with a comprehensive sensitivity analysis.

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Section: Preliminariesmentioning

confidence: 99%

Large-scale group decision-making (LSGDM) for performance measurement of healthcare construction projects: Ordinal Priority Approach

et al. 2022

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“…All of the results leading up to this point were conducted using benchmark datasets and while there are certainly benefits to comparing methods in this way, it does not afford a rich understanding of how any of them perform more generally. This stage of the analysis relies on a method for generating artificial datasets introduced in [25]. In essence, this method is an evolutionary algorithm which acts on entire datasets to explore the space in which potentially all possible datasets exist.…”

Section: Artificial Datasetsmentioning

confidence: 99%

A novel initialisation based on hospital-resident assignment for the k-modes algorithm

Wilde¹,

Knight²,

Gillard³

2020

Preprint

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This paper presents a new way of selecting an initial solution for the k-modes algorithm that allows for a notion of mathematical fairness and a leverage of the data that the common initialisations from literature do not. The method, which utilises the Hospital-Resident Assignment Problem to find the set of initial cluster centroids, is compared with the current initialisations on both benchmark datasets and a body of newly generated artificial datasets. Based on this analysis, the proposed method is shown to outperform the other initialisations in the majority of cases, especially when the number of clusters is optimised. In addition, we find that our method outperforms the leading established method specifically for low-density data.

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A novel initialisation based on hospital-resident assignment for the $$k$$-modes algorithm

2023

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This paper presents a new way of selecting an initialisation for the $$k$$ k -modes algorithm that allows for a notion of game theoretic fairness that classic initialisations, namely those by Huang and Cao, do not. Our new method utilises the hospital-resident assignment problem to find the set of initial cluster centroids which we compare with two classical initialisation methods for $$k$$ k -modes: the original presented by Huang and the next most popular method of Cao and co-authors. To highlight the merits of our proposed method, two stages of analysis are presented. It is demonstrated that the proposed method is often able to offer computational speed-up of the order of $$50\%$$ 50 % . Improved clustering, in terms of a commonly used cost-function, was witnessed in several cases and can be of the order of $$10\%$$ 10 % , particularly for more complex datasets.

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Evolutionary dataset optimisation: learning algorithm quality through evolution

Cited by 8 publications

References 36 publications

Large-scale group decision-making (LSGDM) for performance measurement of healthcare construction projects: Ordinal Priority Approach

Large-scale group decision-making (LSGDM) for performance measurement of healthcare construction projects: Ordinal Priority Approach

A novel initialisation based on hospital-resident assignment for the k-modes algorithm

A novel initialisation based on hospital-resident assignment for the $$k$$-modes algorithm

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