Inferring decision trees using the minimum description length principle
Information and Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Learning by discovering concept hierarchies
Artificial Intelligence
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
Constructing X-of-n Attributes With A Genetic Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Evolutionary Constructive Induction
IEEE Transactions on Knowledge and Data Engineering
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
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
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Feature Construction and Feature Selection in Presence of Attribute Interactions
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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When primitive data representation yields attribute interactions, learning requires feature construction. MFE2/GA, a GA-based feature construction has been shown to learn more accurately than others when there exist several complex attribute interactions. A new fitness function, based on the principle of Minimum Description Length (MDL), is proposed and implemented as part of the MFE3/GA system. Since the individuals of the GA population are collections of new features constructed to change the representation of data, an MDL-based fitness considers not only the part of data left unexplained by the constructed features (errors), but also the complexity of the constructed features as a new representation (theory). An empirical study shows the advantage of the new fitness over other fitness not based on MDL, and both are compared to the performance baselines provided by relevant systems.