Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
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
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Revised report on the algorithm language ALGOL 60
Communications of the ACM
Data Mining Using Grammar-Based Genetic Programming and Applications
Data Mining Using Grammar-Based Genetic Programming and Applications
A perspective view and survey of meta-learning
Artificial Intelligence Review
Is Combining Classifiers Better than Selecting the Best One
ICML '02 Proceedings of the Nineteenth International Conference 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
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Evolutionary Computation
A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Automatically evolving rule induction algorithms
ECML'06 Proceedings of the 17th European conference on Machine Learning
An imbalanced data rule learner
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
Hi-index | 0.00 |
It is well-known that no classification algorithm is the best in all application domains. The conventional approach for coping with this problem consists of trying to select the best classification algorithm for the target application domain. We propose a refreshing departure from this approach, consisting of automatically creating a rule induction algorithm tailored to the target application domain. This work proposes a grammar-based genetic programming (GGP) system to perform "algorithm construction". The GGP is used to build a complete rule induction algorithm tailored to 5 well-known UCI data sets and a protein data set, where the goal is to predict whether or not a protein presents postsynaptic activity. The results show that the rule induction algorithms automatically constructed by the GGP are competitive with well-known human-designed rule induction algorithms. Moreover, in the postsynaptic case study, the GGP was more successful than the human-designed algorithms in discovering accurate rules predicting the minority class - whose prediction is more difficult and tends to be more important to the user than the prediction of the majority class.