A4 Article in conference proceedings
Improving the Competency of Classifiers through Data Generation (2001)


Skrypnyk, I., & Viktor, H. (2001). Improving the Competency of Classifiers through Data Generation. In V. Kůrková, R. Neruda, M. Kárný, & N. C. Steele (Eds.), Proceedings 5th Int. Conf. on Artificial neural Networks and Genetic Algorithms ICANNGA'01, Prague, Czech Republic, April 2001. Springer. https://doi.org/10.1007/978-3-7091-6230-9_13


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Publication details

All authors or editorsSkrypnyk, Iryna; Viktor, Herna

Parent publicationProceedings 5th Int. Conf. on Artificial neural Networks and Genetic Algorithms ICANNGA'01, Prague, Czech Republic, April 2001

Parent publication editorsKůrková, Věra; Neruda, Roman; Kárný, Miroslav; Steele, Nigel C.

Place and date of conferencePrague, Czech Republic2001

ISBN978-3-211-83651-4

eISBN978-3-7091-6230-9

Publication year2001

Number of pages in the book506

PublisherSpringer

Place of PublicationVienna

Publication countryAustria

Publication languageEnglish

DOIhttps://doi.org/10.1007/978-3-7091-6230-9_13

Publication open accessNot open

Publication channel open access


Abstract

This paper describes a hybrid approach in which sub-symbolic neural networks and symbolic machine learning algorithms are grouped into an ensemble of classifiers. Initially each classifier determines which portion of the data it is most competent in. The competency information is used to generated new data that are used for further training and prediction. The application of this approach in a difficult to learn domain shows an increase in the predictive power, in terms of the accuracy and level of competency of both the ensemble and the component classifiers.


Free keywordsproblem domain; training instance; data generation process; disjunctive normal form; decision tree algorithm


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Last updated on 2023-14-12 at 16:02