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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.
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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.
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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. 
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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.
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Heuristics01:21

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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.
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Uncertainty in Measurement: Accuracy and Precision03:37

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Related Experiment Video

Updated: Jun 29, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy.

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
Summary
This summary is machine-generated.

Optimizing machine learning models with metaheuristic algorithms significantly improves software development effort estimation accuracy. This approach enhances ensemble models by fine-tuning hyperparameters and ensemble weights for better project resource management.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Accurate software development effort estimation (SDEE) is crucial for effective project management, as inaccuracies lead to resource mismanagement.
  • Machine learning (ML), particularly ensemble effort estimation (EEE), is widely used in SDEE to mitigate bias and subjectivity of individual ML models.
  • The performance of EEE heavily relies on hyperparameter settings and the weighting of individual models, areas with limited prior research on optimization.

Purpose of the Study:

  • To enhance SDEE performance by integrating metaheuristic optimization for hyperparameters and weight assignment within EEE models.
  • To introduce a novel approach, Metaheuristic-optimized Multi-dimensional bagging scheme and Weighted Ensemble (MoMdbWE), for improved accuracy and diversity in ensemble models.

Main Methods:

  • Proposed a Multi-dimensional bagging (Mdb) technique for search space division and hyperparameter optimization.
  • Utilized the Firefly Algorithm (FFA) to determine optimal hyperparameters for base ML algorithms (Random Forest, Support Vector Machine, Deep Neural Network).
  • Implemented FFA-based weight optimization to create a Metaheuristic-optimized weighted ensemble (MoWE) of individual Mdb schemes.

Main Results:

  • The MoMdbWE approach demonstrated significantly enhanced performance across eight effort estimation datasets.
  • Evaluations using MAE, RMSE, MMRE, MdMRE, Pred, accuracy, and effect size confirmed superior results compared to base algorithms and other EEE techniques.
  • Statistical analysis using the Wilcoxon signed-rank test validated the significant performance improvements achieved through FFA optimization.

Conclusions:

  • Metaheuristic optimization of hyperparameters and ensemble weights substantially improves SDEE performance.
  • The proposed MoMdbWE approach offers a robust method for enhancing the accuracy and reliability of software development effort estimation.
  • This research highlights the potential of advanced optimization techniques in addressing challenges within the SDEE domain.