C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Machine Learning
Inference for the Generalization Error
Machine Learning
Machine Learning
A critical review of multi-objective optimization in data mining: a position paper
ACM SIGKDD Explorations Newsletter
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Classification tree analysis using TARGET
Computational Statistics & Data Analysis
Evolving model trees for mining data sets with continuous-valued classes
Expert Systems with Applications: An International Journal
Greedy regression ensemble selection: Theory and an application to water quality prediction
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
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
On the Importance of Comprehensible Classification Models for Protein Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The impact of random samples in ensemble classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
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
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
Information Sciences: an International Journal
Least squares quantization in PCM
IEEE Transactions on Information Theory
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
Towards the automatic design of decision tree induction algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Learning data structure from classes: A case study applied to population genetics
Information Sciences: an International Journal
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
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Automatic design of decision-tree algorithms with evolutionary algorithms
Evolutionary Computation
Information Sciences: an International Journal
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Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications.