International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
An Exact Probability Metric for Decision Tree Splitting and Stopping
Machine Learning
A New Criterion in Selection and Discretization of Attributes for the Generation of Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Machine Learning
On Estimating Probabilities in Tree Pruning
EWSL '91 Proceedings of the European Working Session on Machine Learning
Inference for the Generalization Error
Machine Learning
SPDW: A Software Development Process Performance Data Warehousing Environment
SEW '06 Proceedings of the 30th Annual IEEE/NASA Software Engineering Workshop
Moving towards efficient decision tree construction
Information Sciences: an International Journal
LEGAL-tree: a lexicographic multi-objective genetic algorithm for decision tree induction
Proceedings of the 2009 ACM symposium on Applied Computing
Evolving rule induction algorithms with multi-objective grammar-based genetic programming
Knowledge and Information Systems
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
Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
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
A new node splitting measure for decision tree construction
Pattern Recognition
Lexicographic multi-objective evolutionary induction of decision trees
International Journal of Bio-Inspired Computation
The attribute selection problem in decision tree generation
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Evolutionary model trees for handling continuous classes in machine learning
Information Sciences: an International Journal
A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics
IEEE Transactions on Evolutionary Computation
Towards the automatic design of decision tree induction algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Predicting software maintenance effort through evolutionary-based decision trees
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A grammatical evolution approach for software effort estimation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
| Hi-index | 0.00 |
Software effort prediction is an important task within software engineering. In particular, machine learning algorithms have been widely-employed to this task, bearing in mind their capability of providing accurate predictive models for the analysis of project stakeholders. Nevertheless, none of these algorithms has become the de facto standard for metrics prediction given the particularities of different software projects. Among these intelligent strategies, decision trees and evolutionary algorithms have been continuously employed for software metrics prediction, though mostly independent from each other. A recent work has proposed evolving decision trees through an evolutionary algorithm, and applying the resulting tree in the context of software maintenance effort prediction. In this paper, we raise the search-space level of an evolutionary algorithm by proposing the evolution of a decision-tree algorithm instead of the decision tree itself --- an approach known as hyper-heuristic. Our findings show that the decision-tree algorithm automatically generated by a hyper-heuristic is capable of statistically outperforming state-of-the-art top-down and evolution-based decision-tree algorithms, as well as traditional logistic regression. The ability of generating a highly-accurate comprehensible predictive model is crucial in software projects, considering that it allows the stakeholder to properly manage the team's resources with an improved confidence in the model predictions.