Research graph
References from Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy. Local targets link to admitted publications; unresolved targets remain external evidence.
Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation
2022 · External reference
Case Study on Software Effort Estimation
10.18178/ijiee.2017.7.3.670 · 2017 · External reference
Hyperparameters tuning of ensemble model for software effort estimation
10.1007/s12652-020-02277-4 · 2021 · External reference
Expert judgement as an estimating method
10.1016/0950-5849(95)01045-9 · 1996 · External reference
Missing data techniques in analogy-based software development effort estimation
10.1016/j.jss.2016.04.058 · 2016 · External reference
Systematic literature review of ensemble effort estimation
10.1016/j.jss.2016.05.016 · 2016 · External reference
A systematic review of software development cost estimation studies
10.1109/tse.2007.256943 · 2006 · External reference
Model-based software effort estimation—a robust comparison of 14 algorithms widely used in the data science community
2019 · External reference
Software cost modeling: Some lessons learned
10.1016/0164-1212(79)90020-7 · 1979 · External reference
Empirical study of homogeneous and heterogeneous ensemble models for software development effort estimation
10.1155/2013/312067 · 2013 · External reference
Systematic literature review of machine learning based software development effort estimation models
10.1016/j.infsof.2011.09.002 · 2012 · External reference
Unresolved reference
External reference
On the value of parameter tuning in heterogeneous ensembles effort estimation
10.1007/s00500-017-2945-4 · 2018 · External reference
An Ensemble-Based Framework to Estimate Software Project Effort
2023 · External reference
Heterogeneous Ensemble Model to Optimize Software Effort Estimation Accuracy
10.1109/access.2023.3256533 · 2023 · External reference
Decidability and complexity of threshold description logics induced by concept similarity measures
2017 · External reference
Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades
10.1016/j.cosrev.2020.100288 · 2020 · External reference
Software effort estimation using classical analogy ensembles based on random subspace
2017 · External reference
Global optimization in machine learning: the design of a predictive analytics application
10.1007/s00500-018-3597-8 · 2019 · External reference
Improved estimation of software development effort using classical and fuzzy analogy ensembles
10.1016/j.asoc.2016.08.012 · 2016 · External reference
Balancing accuracy and diversity in ensemble learning using a two-phase artificial bee colony approach
10.1016/j.asoc.2021.107212 · 2021 · External reference
An investigation of artificial neural networks based prediction systems in software project management
10.1016/j.jss.2007.05.011 · 2008 · External reference
Software effort interval prediction via Bayesian inference and synthetic bootstrap resampling
10.1145/3295700 · 2019 · External reference
Unresolved reference
2020 · External reference
Evaluating prediction systems in software project estimation
10.1016/j.infsof.2011.12.008 · 2012 · External reference
Heterogeneous ensembles for software development effort estimation
2016 · External reference
Investigating heterogeneous ensembles with filter feature selection for software effort estimation
10.1145/3143434.3143456 · 2017 · External reference
Analyzing Effect of Ensemble Models on Multi-Layer Perceptron Network for Software Effort Estimation
External reference
Ensembling artificial bee colony with analogy-based estimation to improve software development effort prediction
10.1109/access.2020.2980236 · 2020 · External reference
Enhanced framework for ensemble effort estimation by using recursive‐based classification
10.1049/sfw2.12020 · 2021 · External reference
Software effort estimation using ensemble of hybrid search-based algorithms based on metaheuristic algorithms
2021 · External reference
Performance evaluation of kernel functions based on grid search for support vector regression
2015 · External reference
Efficient and robust automated machine learning
2015 · External reference
Initializing hyper-parameter tuning with a metaheuristic-ensemble method: a case study using time-series weather data
2022 · External reference
Ensemble effort estimation using selection and genetic algorithms
10.1504/ijcat.2018.094061 · 2018 · External reference
Optimizing software effort estimation models using firefly algorithm
2019 · External reference
A novel PSO based back propagation learning-MLP (PSO-BP-MLP) for classification
2015 · External reference
Implementation of Bat Algorithm for COCOMO II Optimization
10.1109/isemantic.2018.8549699 · 2018 · External reference
Evaluating different families of prediction methods for estimating software project outcomes
2016 · External reference
A hybrid model of wavelet neural network and metaheuristic algorithm for software development effort estimation
2019 · External reference
An ensemble-based model for predicting agile software development effort
10.1007/s10664-018-9647-0 · 2019 · External reference
An evolutionary ensemble analogy‐based software effort estimation
2022 · External reference
Investigating the use of random forest in software effort estimation
10.1016/j.procs.2019.01.042 · 2019 · External reference
Random forests, decision trees, and categorical predictors: the" absent levels" problem
2018 · External reference
Splitting on categorical predictors in random forests
10.7717/peerj.6339 · 2019 · External reference
Estimating software development efforts using a random forest-based stacked ensemble approach
10.3390/electronics10101195 · 2021 · External reference
Support vector regression for predicting software enhancement effort
10.1016/j.infsof.2018.01.003 · 2018 · External reference
Unresolved reference
2011 · External reference
Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation
2020 · External reference
Estimation of software project effort with support vector regression
10.1016/j.neucom.2005.12.119 · 2006 · External reference
Research patterns and trends in software effort estimation
10.1016/j.infsof.2017.06.002 · 2017 · External reference
Metaheuristic algorithms in optimizing deep neural network model for software effort estimation
10.1109/access.2021.3072380 · 2021 · External reference
Analysis of L2 Regularization Hyper Parameter for Stock Price Prediction
10.3126/jist.v26i1.37830 · 2021 · External reference
A Systematic Mapping Study On Software Effort Estimation
2020 · External reference
Nature inspired optimization and its application to engineering
2021 · External reference
editor Firefly algorithms for multimodal optimization
2009 · External reference
Firefly algorithm, stochastic test functions and design optimisation
10.1504/ijbic.2010.032124 · 2010 · External reference
A comparative performance study of hybrid firefly algorithms for automatic data clustering
10.1109/access.2020.3006173 · 2020 · External reference
Unresolved reference
2009 · External reference
Evolving data-adaptive support vector machines for binary classification
10.1016/j.knosys.2021.107221 · 2021 · External reference
A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model
10.1002/dac.3394 · 2018 · External reference
10.1007/978-3-540-28645-5_41
10.1007/978-3-540-28645-5_41 · External reference
Unresolved reference
2017 · External reference
Local versus global lessons for defect prediction and effort estimation
10.1109/tse.2012.83 · 2012 · External reference
Software function, source lines of code, and development effort prediction: a software science validation
10.1109/tse.1983.235271 · 1983 · External reference
Unresolved reference
1989 · External reference
Robust regression for developing software estimation models
10.1016/0164-1212(94)90110-4 · 1994 · External reference
Unresolved reference
2012 · External reference
Software engineering economics
10.1109/tse.1984.5010193 · 1984 · External reference
Estimating software project effort using analogies
10.1109/32.637387 · 1997 · External reference
An empirical study of maintenance and development estimation accuracy
10.1016/s0164-1212(02)00021-3 · 2002 · External reference
Software productivity and effort prediction with ordinal regression
10.1016/j.infsof.2004.05.001 · 2005 · External reference
Unresolved reference
2015 · External reference
Investigating the impact of data normalization on classification performance
10.1016/j.asoc.2019.105524 · 2020 · External reference
A pragmatic ensemble learning approach for effective software effort estimation
2021 · External reference
Software enhancement effort estimation using correlation-based feature selection and stacking ensemble method
10.1007/s10586-021-03447-5 · 2022 · External reference
An effective approach for software project effort and duration estimation with machine learning algorithms
10.1016/j.jss.2017.11.066 · 2018 · External reference
A simulation study of the model evaluation criterion MMRE
10.1109/tse.2003.1245300 · 2003 · External reference
A power primer
10.1037/0033-2909.112.1.155 · 1992 · External reference
Statistical comparisons of classifiers over multiple data sets
2006 · External reference
Performance of nonparametric wilcoxon test with reference to the samples with singularities
2019 · External reference
What is the proper way to apply the multiple comparison test?
10.4097/kja.d.18.00242 · 2018 · External reference
Hypothesis testing, type I and type II errors
10.4103/0972-6748.62274 · 2009 · External reference
Software development effort estimation using random forests: an empirical study and evaluation
10.22266/ijies2018.1231.30 · 2018 · External reference
Ensemble Effort Estimation using dynamic selection
10.1016/j.jss.2021.110904 · 2021 · External reference
10.1007/978-1-4419-9326-7
10.1007/978-1-4419-9326-7 · 2012 · External reference
