Hyper-Parameter Tuning of Classification and Regression Trees for Software Effort Estimation

[1]  Mohammad Azzeh,et al.  Software effort estimation based on optimized model tree , 2011, Promise '11.

[2]  Leandro L. Minku A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation , 2019, Empirical Software Engineering.

[3]  Xin Yao,et al.  Software Effort Interval Prediction via Bayesian Inference and Synthetic Bootstrap Resampling , 2019, ACM Trans. Softw. Eng. Methodol..

[4]  Shane McIntosh,et al.  The Impact of Automated Parameter Optimization on Defect Prediction Models , 2018, IEEE Transactions on Software Engineering.

[5]  Bart Baesens,et al.  Data Mining Techniques for Software Effort Estimation: A Comparative Study , 2012, IEEE Transactions on Software Engineering.

[6]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[7]  Çağatay Çatal,et al.  Performance tuning for machine learning-based software development effort prediction models , 2019, TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES.

[8]  Yoshua Bengio,et al.  Algorithms for Hyper-Parameter Optimization , 2011, NIPS.

[9]  Christopher J. Lokan,et al.  The usage of ISBSG data fields in software effort estimation: A systematic mapping study , 2016, J. Syst. Softw..

[10]  A. Scott,et al.  A Cluster Analysis Method for Grouping Means in the Analysis of Variance , 1974 .

[11]  Abdelaziz Marzak,et al.  Systematic Review Study of Decision Trees based Software Development Effort Estimation , 2020 .

[12]  Li Yang,et al.  On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice , 2020, Neurocomputing.

[13]  ShenXipeng,et al.  Tuning for software analytics , 2016 .

[14]  Mark Harman,et al.  Exact Mean Absolute Error of Baseline Predictor, MARP0 , 2016, Inf. Softw. Technol..

[15]  Kaushal Chari,et al.  An ensemble-based model for predicting agile software development effort , 2018, Empirical Software Engineering.

[16]  Tim Menzies,et al.  Tuning for Software Analytics: is it Really Necessary? , 2016, Inf. Softw. Technol..

[17]  Stephen G. MacDonell,et al.  Evaluating prediction systems in software project estimation , 2012, Inf. Softw. Technol..

[18]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[19]  Min Xie,et al.  An empirical analysis of data preprocessing for machine learning-based software cost estimation , 2015, Inf. Softw. Technol..

[20]  Emilia Mendes,et al.  How effective is Tabu search to configure support vector regression for effort estimation? , 2010, PROMISE '10.

[21]  Xin Yao,et al.  The potential benefit of relevance vector machine to software effort estimation , 2014, PROMISE.

[22]  Shai Ben-David,et al.  Understanding Machine Learning: From Theory to Algorithms , 2014 .

[23]  Yong Hu,et al.  Systematic literature review of machine learning based software development effort estimation models , 2012, Inf. Softw. Technol..

[24]  Xin Yao,et al.  The impact of parameter tuning on software effort estimation using learning machines , 2013, PROMISE.

[25]  Christian Quesada-López,et al.  Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation , 2020, PROMISE.