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相关概念视频

Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
198
Heuristics01:21

Heuristics

90
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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相关实验视频

Updated: Jun 29, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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将努力估计与元启发式超参数和重量优化相结合,以实现准确度.

Anum Yasmin1, Wasi Haider Butt1, Ali Daud2

  • 1Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.

PloS one
|April 4, 2024
PubMed
概括

使用元启发算法优化机器学习模型显著提高了软件开发工作精度估计的准确性. 这种方法通过微调超参数和集合权重来增强组合模型,以更好地管理项目资源.

科学领域:

  • 计算机科学 计算机科学
  • 软件工程 软件工程 软件工程
  • 人工智能的人工智能

背景情况:

  • 准确的软件开发努力估计 (SDEE) 对于有效的项目管理至关重要,因为不准确会导致资源管理不当.
  • 机器学习 (ML),特别是集体努力估计 (EEE),在SDEE中被广泛使用,以减轻单个ML模型的偏差和主观性.
  • 电气设备的性能在很大程度上依赖于超参数设置和个别模型的权重,这些领域之前对优化研究有限.

研究的目的:

  • 通过在EEE模型中集成超参数和权重赋值的元启发式优化来提高SDEE性能.
  • 引入一种新的方法,即Metaheuristic优化的多维包装方案和加权合并 (MoMdbWE),以提高合并模型的准确性和多样性.

主要方法:

  • 为搜索空间划分和超参数优化提出了一个多维包装 (Mdb) 技术.
  • 利用火算法 (FFA) 来确定基础ML算法 (随机森林,支持矢量机器,深度神经网络) 的最佳超参数.
  • 实现了基于FFA的权重优化,以创建单个Mdb方案的Metaheuristic优化权重组合 (MoWE).

主要成果:

  • 在8个努力估计数据集中,MoMdbWE方法显示显著提高了性能.
  • 使用MAE,RMSE,MMRE,MdMRE,Pred,精度和效果大小的评估证实了与基础算法和其他EEE技术相比的优异结果.

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  • 使用Wilcoxon签名等级测试的统计分析验证了通过FFA优化实现的显著性能改善.

    • 结论:

    • 超参数和集合权重的metaheuristic优化大大提高了SDEE的性能.
    • 拟议的MoMdbWE方法提供了一个强大的方法来提高软件开发工作量估计的准确性和可靠性.
    • 这项研究强调了先进的优化技术在应对SDEE领域的挑战方面的潜力.