Performance of Nonparametric Wilcoxon Test with Reference to the Samples with Singularities

Kochengin, A. E.; Chrysostomou, George; Shikhin, Vladimir

doi:10.1109/cts48763.2019.8973318

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Statistical evaluation is very crucial part of any research, since it provides evidence that performance achieved by a model is significant enough to be compared. To ensure if proposed model’s results are statistically significant than other comparing models [ 80 ], Wilcoxon test is used [ 81 ]. Wilcoxon test is included to verify the significance difference between error measures of all comparing models.…”

Section: Methodsmentioning

confidence: 99%

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Yasmin,

Haider Butt,

Daud

2024

PLoS ONE

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Software development effort estimation (SDEE) is recognized as vital activity for effective project management since under or over estimating can lead to unsuccessful utilization of project resources. Machine learning (ML) algorithms are largely contributing in SDEE domain, particularly ensemble effort estimation (EEE) works well in rectifying bias and subjectivity to solo ML learners. Performance of EEE significantly depends on hyperparameter composition as well as weight assignment mechanism of solo learners. However, in EEE domain, impact of optimization in terms of hyperparameter tunning as well as weight assignment is explored by few researchers. This study aims in improving SDEE performance by incorporating metaheuristic hyperparameter and weight optimization in EEE, which enables accuracy and diversity to the ensemble model. The study proposed Metaheuristic-optimized Multi-dimensional bagging scheme and Weighted Ensemble (MoMdbWE) approach. This is achieved by proposed search space division and hyperparameter optimization method named as Multi-dimensional bagging (Mdb). Metaheuristic algorithm considered for this work is Firefly algorithm (FFA), to get best hyperparameters of three base ML algorithms (Random Forest, Support vector machine and Deep Neural network) since FFA has shown promising results of fitness in terms of MAE. Further enhancement in performance is achieved by incorporating FFA-based weight optimization to construct Metaheuristic-optimized weighted ensemble (MoWE) of individual multi-dimensional bagging schemes. Proposed scheme is implemented on eight frequently utilized effort estimation datasets and results are evaluated by 5 error metrices (MAE, RMSE, MMRE, MdMRE, Pred), standard accuracy and effect size along with Wilcox statistical test. Findings confirmed that the use of FFA optimization for hyperparameter (with search space sub-division) and for ensemble weights, has significantly enhanced performance in comparison with individual base algorithms as well as other homogeneous and heterogenous EEE techniques.

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Section: Methodsmentioning

confidence: 99%

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Yasmin,

Haider Butt,

Daud

2024

PLoS ONE

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“…With the purpose of further assessing the performance of these heuristic algorithms and proving a significant improvement of CASSA compared with the other four algorithms, a non-parametric statistical test is performed with the Wilcoxon [ 38 ] rank sum test set at a significance level of α = 5%. It is generally considered that when p < 5%, it is significantly different, while when p ≥ 5%, it is not significantly different.…”

Section: Experiments For Benchmark Functionsmentioning

confidence: 99%

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

Liu

Shu

Liang

et al. 2021

Sensors

181

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The unmanned aerial vehicle (UAV) route planning problem mainly centralizes on the process of calculating the best route between the departure point and target point as well as avoiding obstructions on route to avoid collisions within a given flight area. A highly efficient route planning approach is required for this complex high dimensional optimization problem. However, many algorithms are infeasible or have low efficiency, particularly in the complex three-dimensional (3d) flight environment. In this paper, a modified sparrow search algorithm named CASSA has been presented to deal with this problem. Firstly, the 3d task space model and the UAV route planning cost functions are established, and the problem of route planning is transformed into a multi-dimensional function optimization problem. Secondly, the chaotic strategy is introduced to enhance the diversity of the population of the algorithm, and an adaptive inertia weight is used to balance the convergence rate and exploration capabilities of the algorithm. Finally, the Cauchy–Gaussian mutation strategy is adopted to enhance the capability of the algorithm to get rid of stagnation. The results of simulation demonstrate that the routes generated by CASSA are preferable to the sparrow search algorithm (SSA), particle swarm optimization (PSO), artificial bee colony (ABC), and whale optimization algorithm (WOA) under the identical environment, which means that CASSA is more efficient for solving UAV route planning problem when taking all kinds of constraints into consideration.

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“…We adopt the nonparametric Wilcoxon rank sum test set to determine significant differences between the results obtained by the DGA-ACBIO in solving the multi-tea field scheduling problem and those of the other algorithms (Kochengin et al, 2019). The significance level is set at α = 5%, indicating significant differences for p < 5% and vice versa.…”

Section: Dynamic Genetic Algorithm With Ant Colony Binary Iterative O...mentioning

confidence: 99%

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

Liu

Zhang

et al. 2022

Front. Plant Sci.

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Performance of Nonparametric Wilcoxon Test with Reference to the Samples with Singularities

Cited by 4 publications

References 3 publications

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

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