Training a 3-node neural network is NP-complete
COLT '88 Proceedings of the first annual workshop on Computational learning theory
Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Genetic neural networks on MIMD computers
Genetic neural networks on MIMD computers
C4.5: programs for machine learning
C4.5: programs for machine learning
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Machine Learning
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
A Tool to Obtain a Hierarchical Qualitative Rules form Quantitative Data
IEA/AIE '98 Proceedings of the 11th international conference on Industrial and engineering applications of artificial intelligence and expert systems: methodology and tools in knowledge-based systems
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
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This paper describes a new approach, HIerarchical DEcision Rules (HIDER), for learning generalizable rules in continuous and discrete domains based on evolutionary algorithms. The main contributions of our approach are the integration of both binary and real evolutionary coding; the use of specific operators; the relaxing coefficient to construct more flexible classifiers by indicating how general, with respect to the errors, decision rules must be; the coverage factor in the fitness function, which makes possible a quick expansion of the rule size; and the implicit hierarchy when rules are being obtained. HIDER is accuracy-aware since it can control the maximum allowed error for each decision rule. We have tested our system on real data from the UCI Repository. The results of a 10-fold cross-validation are compared to C4.5's and they show a significant improvement with respect to the number of rules and the error rate.