Optimal Partitioning for Classification and Regression Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Machine Learning
Breeding Decision Trees Using Evolutionary Techniques
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Application of Genetic Programming to Induction of Linear Classification Trees
Proceedings of the European Conference on Genetic Programming
Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees
Proceedings of the European Conference on Genetic Programming
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Inference for the Generalization Error
Machine Learning
Parallel Genetic Programming for Decision Tree Induction
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
GA Tree: genetically evolved decision trees
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Genetic Programming for data classification: partitioning the search space
Proceedings of the 2004 ACM symposium on Applied computing
A critical review of multi-objective optimization in data mining: a position paper
ACM SIGKDD Explorations Newsletter
Diversification for better classification trees
Computers and Operations Research
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
Evolutionary Induction of Decision Trees for Misclassification Cost Minimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
LEGAL-tree: a lexicographic multi-objective genetic algorithm for decision tree induction
Proceedings of the 2009 ACM symposium on Applied Computing
Generating better decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Skewing: an efficient alternative to lookahead for decision tree induction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Study on Constructing Generalized Decision Tree by Using DNA Coding Genetic Algorithm
WISM '09 Proceedings of the 2009 International Conference on Web Information Systems and Mining
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
Improving induction decision trees with parallel genetic programming
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
Evolutionary model trees for handling continuous classes in machine learning
Information Sciences: an International Journal
Towards the automatic design of decision tree induction algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Evolutionary induction of cost-sensitive decision trees
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Mixed decision trees: an evolutionary approach
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
A study on the importance of and time spent on different modeling steps
ACM SIGKDD Explorations Newsletter
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
Look-ahead based fuzzy decision tree induction
IEEE Transactions on Fuzzy Systems
A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
A multiobjective evolutionary programming framework for graph-based data mining
Information Sciences: an International Journal
An interpretable classification rule mining algorithm
Information Sciences: an International Journal
Automatic design of decision-tree algorithms with evolutionary algorithms
Evolutionary Computation
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Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.