Learning hard concepts through constructive induction: framework and rationale
Computational Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Function decomposition in machine learning
Machine Learning and Its Applications
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Feature Space Transformation Using Genetic Algorithms
IEEE Intelligent Systems
Attribute Selection with a Multi-objective Genetic Algorithm
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
Khiops: A Statistical Discretization Method of Continuous Attributes
Machine Learning
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Constructive induction and genetic algorithms for learning concepts with complex interaction
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
A genetic algorithms based multi-objective neural net applied to noisy blast furnace data
Applied Soft Computing
Applied Soft Computing
Reducing complex attribute interaction through non-algebraic feature construction
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
DARA: Data Summarisation with Feature Construction
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Searching for interacting features
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Feature construction and selection using genetic programming and a genetic algorithm
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Projecting financial data using genetic programming in classification and regression tasks
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
Journal of Ambient Intelligence and Smart Environments
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Real-world data are often prepared for purposes other than data mining and machine learning and, therefore, are represented by primitive attributes. When data representation is primitive, preprocessing data before looking for patterns becomes necessary. The low-level primitive representation of real-world problems facilitates the existence of complex interactions among attributes. If lack of domain experts prevents traditional methods to uncover patterns in data due to complex attribute interactions, then the use of soft computing techniques such as genetic algorithms becomes necessary. This article introduces MFE3/GA^D^R, a data reduction method derived from the learning preprocessing system MFE3/GA. The method restructures the primitive data representation by capturing and compacting hidden information into new features in order to highlight regularities to the learner. We thoroughly analyze the empirical results obtained on the poker hand data set. The results show that this approach successfully compacts the set of low-level primitive attributes into a smaller set of highly informative features which outline patterns to the learner; thus, the new approach provides data reduction and yields learning a smaller and more accurate classifier.