Boolean Feature Discovery in Empirical Learning
Machine Learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
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
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Feature Space Transformation Using Genetic Algorithms
IEEE Intelligent Systems
Constructing X-of-n Attributes With A Genetic Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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
Generation of attributes for learning algorithms
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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
Evolutionary multi-feature construction for data reduction: A case study
Applied Soft Computing
A real-coded genetic algorithm for constructive induction
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Projecting financial data using genetic programming in classification and regression tasks
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Strengthening learning algorithms by feature discovery
Information Sciences: an International Journal
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Constructive Induction is the process of transforming the original representation of hard concepts with complex interaction into a representation that highlights regularities. Most Constructive Induction methods apply a greedy strategy to find interacting attributes and then construct functions over them. This approach fails when complex interaction exists among attributes and the search space has high variation. In this paper, we illustrate the importance of applying Genetic Algorithms as a global search strategy for these methods and present MFE2/GA1, while comparing it with other GA-based Constructive Induction methods. We empirically analyze our Genetic Algorithm's operators and compare MFE2/GA with greedy-based methods. We also performed experiments to evaluate the presented method when concept has attributes participating in more than one complex interaction. In experiments that are conducted, MFE2/GA successfully finds interacting attributes and constructs functions to represent interactions. Results show the advantage of using Genetic Algorithms for Constructive Induction when compared with greedy-based methods.