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
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
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
Inference for the Generalization Error
Machine Learning
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
A critical review of multi-objective optimization in data mining: a position paper
ACM SIGKDD Explorations Newsletter
Using evolutionary algorithms for the unit testing of object-oriented software
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
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
Bioinformatics
On the Importance of Comprehensible Classification Models for Protein Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Automatically evolving rule induction algorithms
ECML'06 Proceedings of the 17th European conference on Machine Learning
Evolutionary model tree induction
Proceedings of the 2010 ACM Symposium on Applied Computing
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
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
Hybrid dynamic k-nearest-neighbour and distance and attribute weighted method for classification
International Journal of Computer Applications in Technology
Cooperative game-based routing approach for wireless sensor network
International Journal of Computer Applications in Technology
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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Among the several tasks that evolutionary algorithms have successfully employed, the induction of classification rules and decision trees has been shown to be a relevant approach for several application domains. Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, conventionally used decision trees induction algorithms present limitations due to the strategy they usually implement: recursive top-down data partitioning through a greedy split evaluation. The main problem with this strategy is quality loss during the partitioning process, which can lead to statistically insignificant rules. In this paper, we propose a new GA-based algorithm for decision tree induction. The proposed algorithm aims to prevent the greedy strategy and to avoid converging to local optima. For such, it is based on a lexicographic multi-objective approach. In order to evaluate the proposed algorithm, it is compared with a well-known and frequently used decision tree induction algorithm using different public datasets. According to the experimental results, the proposed algorithm is able to avoid the previously described problems, reporting accuracy gains. Even more important, the proposed algorithm induced models with a significantly reduction in the complexity considering tree sizes.