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.)
Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Data Mining: the search for knowledge in databases.
Data Mining: the search for knowledge in databases.
Evolutionary multi-feature construction for data reduction: A case study
Applied Soft Computing
Data & Knowledge Engineering
Using biclustering for automatic attribute selection to enhance global visualization
VIEW'06 Proceedings of the 1st first visual information expert conference on Pixelization paradigm
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Journal of Systems Architecture: the EUROMICRO Journal
Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
Journal of Ambient Intelligence and Smart Environments
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In this paper we address the problem of multi-objective attribute selection in data mining. We propose a multi-objective genetic algorithm (GA) based on the wrapper approach to discover the best subset of attributes for a given classification algorithm, namely C4.5, a well-known decision-tree algorithm. The two objectives to be minimized are the error rate and the size of the tree produced by C4.5. The proposed GA is a multi-objective method in the sense that it discovers a set of non-dominated solutions (attribute subsets), according to the concept of Pareto dominance.