Self-configuring hybrid evolutionary algorithm for fuzzy imbalanced classification with adaptive instance selection

• Autorzy: Stanovov V. , Semenkin E. , Semenkina O.

Identyfikatory

DOI

10.1515/jaiscr-2016-0013

• Języki publikacji: EN

Abstrakty

EN

A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.

Słowa kluczowe

EN

fuzzy classification; instance selection; genetic fuzzy system; self-configuration

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2016
• Tom: Vol. 6, No. 3
• Strony: 173--188
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Stanovov V.

• Institute of Computer Science and Telecommunications, Siberian State Aerospace University Krasnoyarskii rabochii ave. 31, 660014, Krasnoyarsk, Russian Federation

autor

Semenkin E.

• Institute of Computer Science and Telecommunications, Siberian State Aerospace University Krasnoyarskii rabochii ave. 31, 660014, Krasnoyarsk, Russian Federation

autor

Semenkina O.

• Institute of Computer Science and Telecommunications, Siberian State Aerospace University Krasnoyarskii rabochii ave. 31, 660014, Krasnoyarsk, Russian Federation

Bibliografia

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.