Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Selecting typical instances in instance-based learning
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Machine Learning
Data Mining Using Grammar-Based Genetic Programming and Applications
Data Mining Using Grammar-Based Genetic Programming and Applications
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
CAMLET: A Platform for Automatic Composition of Inductive Learning Systems Using Ontologies
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Rule induction and instance-based learning a unified approach
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Lexicographic multi-objective evolutionary induction of decision trees
International Journal of Bio-Inspired Computation
Towards the automatic design of decision tree induction algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
DepthLimited crossover in GP for classifier evolution
Computers in Human Behavior
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Research in the rule induction algorithm field produced many algorithms in the last 30 years. However, these algorithms are usually obtained from a few basic rule induction algorithms that have been often changed to produce better ones. Having these basic algorithms and their components in mind, this work proposes the use of Grammar-based Genetic Programming (GGP) to automatically evolve rule induction algorithms. The proposed GGP is evaluated in extensive computational experiments involving 11 data sets. Overall, the results show that effective rule induction algorithms can be automatically generated using GGP. The automatically evolved rule induction algorithms were shown to be competitive with well-known manually designed ones. The proposed approach of automatically evolving rule induction algorithms can be considered a pioneering one, opening a new kind of research area.