Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Multistrategy constructive induction
Multistrategy constructive induction
Learning despite complex attribute interaction: an approach based on relational operators
Learning despite complex attribute interaction: an approach based on relational operators
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Constructive induction and genetic algorithms for learning concepts with complex interaction
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
Foundations and Advances in Data Mining (Studies in Fuzziness and Soft Computing) (Studies in Fuzziness and Soft Computing)
Fuzzy classifier design using genetic algorithms
Pattern Recognition
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
A new approach to generate weighted fuzzy rules using genetic algorithms for estimating null values
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Genetic optimization of order scheduling with multiple uncertainties
Expert Systems with Applications: An International Journal
Generation of attributes for learning algorithms
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Class-dependent projection based method for text categorization
Pattern Recognition Letters
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Constructive Induction (CI) is a process applied to representation space prior to learning algorithms. This process transforms original representation space into a representation that highlights regularities. In this new improved space learning algorithms work more effectively, generating better solutions. Most CI methods apply a greedy strategy to improve representation space. Greedy methods might converge to local optima, when search space is complex. Genetic Algorithms (GA) as a global search strategy is more effective in such situations. In this paper, a real-coded GA (RGACI) model is represented for CI. This model optimizes the representation space by discretization of feature's values, constructing new features with a GA and evaluation and selection of features upon a PNN Classifier accuracy. Results reveal that PNN Classifier accuracy will improved considerably after it is integrated with RGACI model.