Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Diversification for better classification trees
Computers and Operations Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
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
LEGAL-tree: a lexicographic multi-objective genetic algorithm for decision tree induction
Proceedings of the 2009 ACM symposium on Applied Computing
On the Importance of Comprehensible Classification Models for Protein Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
Lexicographic multi-objective evolutionary induction of decision trees
International Journal of Bio-Inspired Computation
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
Evolutionary model trees for handling continuous classes in machine learning
Information Sciences: an International Journal
Predicting software maintenance effort through evolutionary-based decision trees
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Evolutionary design of decision trees for medical application
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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 with an acceptable level of predictive performance. Since generating optimal model trees is a NP-Complete problem, the traditional model tree induction algorithms make use of a greedy heuristic, which may not converge to the global optimal solution. We propose the use of the evolutionary algorithms paradigm (EA) as an alternate heuristic to generate model trees in order to improve the convergence to global optimal solutions. We test the predictive performance of this new approach using public UCI datasets, and compare the results with traditional greedy regression/model trees induction algorithms.