Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Learning by discovering concept hierarchies
Artificial Intelligence
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Feature Space Transformation Using Genetic Algorithms
IEEE Intelligent Systems
Machine Learning
Constructive induction and genetic algorithms for learning concepts with complex interaction
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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
MDL-based fitness for feature construction
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fitness Function Comparison for GA-Based Feature Construction
Current Topics in Artificial Intelligence
Evolutionary multi-feature construction for data reduction: A case study
Applied Soft Computing
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The importance of preprocessing data before looking for patterns is greatest when data representation is primitive. If lack of domain experts prevents the use of highly informative attributes, patterns are hard to uncover due to complex attribute interactions. Feature construction intends to create new features that encapsulate and highlight the hidden interactions. However, its success often relies on the appropriateness of a given set of algebraic operators for expressing the relevant combination of attributes in the current domain. When lacking prior knowledge of appropriate operators, systems use non-algebraic feature construction techniques to extract features directly from training data. The paper analyzes two such systems, MFE2/GA and HINT, concluding that their different design components suggest complementary functionalities. This is supported by an empirical system comparison using synthetic and real-world data where attribute interaction prevails.