Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Programming and Evolvable Machines
Feature Selection Using Consistency Measure
DS '99 Proceedings of the Second International Conference on Discovery Science
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
Evolutionary Constructive Induction
IEEE Transactions on Knowledge and Data Engineering
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature selection for the SVM: An application to hypertension diagnosis
Expert Systems with Applications: An International Journal
A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties
IEEE Transactions on Computers
DARA: Data Summarisation with Feature Construction
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Distributed learning with data reduction
Transactions on computational collective intelligence IV
Feature reduction to speed up malware classification
NordSec'11 Proceedings of the 16th Nordic conference on Information Security Technology for Applications
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Together with increasing sizes of collected data, the problem of feature set reduction becomes more important. Machine learning methods, including classifiers, are sensitive to the training data. One of the known problems is called 'curse of dimensionality'. Some features (attributes) in the collection of data may not be informative so they obstruct the learning process. Removing them is very desirable from the classification quality point of view. In the paper we focus on wrapper approach to feature set reduction. We propose an evolutionary method to feature reduction by means of selection and construction. Genetic Algorithm is used as a tool for feature selection and Gene Expression Programming as a tool of dimensionality reduction by features construction. The paper presents the approach and the results of conducted experiments. Conclusions and future plans end the paper.