C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Inference for the Generalization Error
Machine Learning
A critical review of multi-objective optimization in data mining: a position paper
ACM SIGKDD Explorations Newsletter
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
SPDW: A Software Development Process Performance Data Warehousing Environment
SEW '06 Proceedings of the 30th Annual IEEE/NASA Software Engineering Workshop
LEGAL-tree: a lexicographic multi-objective genetic algorithm for decision tree induction
Proceedings of the 2009 ACM symposium on Applied Computing
Evolutionary software engineering, a review
Applied Soft Computing
Issues on Estimating Software Metrics in a Large Software Operation
SEW '08 Proceedings of the 2008 32nd Annual IEEE Software Engineering Workshop
Using genetic programming to improve software effort estimation based on general data sets
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Evolutionary model tree induction
Proceedings of the 2010 ACM Symposium on Applied Computing
Lexicographic multi-objective evolutionary induction of decision trees
International Journal of Bio-Inspired Computation
Evolutionary model trees for handling continuous classes in machine learning
Information Sciences: an International Journal
A grammatical evolution approach for software effort estimation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Information Sciences: an International Journal
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Software effort prediction has been a challenge for researchers throughout the years. Several approaches for producing predictive models from collected data have been proposed, although none has become standard given the specificities of different software projects. The most commonly employed strategy for estimating software effort, the multivariate linear regression technique has numerous shortcomings though, which motivated the exploration of many machine learning techniques. Among the researched strategies, decision trees and evolutionary algorithms have been increasingly employed for software effort prediction, though independently. In this paper, we propose employing an evolutionary algorithm to generate a decision tree tailored to a software effort data set provided by a large worldwide IT company. Our findings show that evolutionarily-induced decision trees statistically outperform greedily-induced ones, as well as traditional logistic regression. Moreover, an evolutionary algorithm with a bias towards comprehensibility can generate trees which are easier to be interpreted by the project stakeholders, and that is crucial in order to improve the stakeholder's confidence in the final prediction.