TY - GEN
T1 - The smaller, the better
T2 - 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion
AU - Nalepa, Jakub
AU - Kawulok, Michal
N1 - Publisher Copyright:
© 2016 Copyright held by the owner/author(s).
PY - 2016/7/20
Y1 - 2016/7/20
N2 - Support vector machine (SVM) is a supervised classifier which has been applied for solving a wide range of pattern recognition problems. However, training of SVMs may easily become a bottleneck, because of its time and memory requirements. Enduring this issue is a vital research topic, especially in the era of big data. In this abstract, we present our adaptive memetic algorithm for selection of refined (significantly smaller) SVM training sets. The algorithm - being a hybrid of an adaptive genetic algorithm and some refinement procedures - exploits the knowledge about the training set vectors extracted before the evolution, and attained dynamically during the search. The results obtained for several real-life, benchmark, and artificial datasets showed that our approach outperforms the other state-of-the-art techniques, and is able to extract very high-quality SVM training sets.
AB - Support vector machine (SVM) is a supervised classifier which has been applied for solving a wide range of pattern recognition problems. However, training of SVMs may easily become a bottleneck, because of its time and memory requirements. Enduring this issue is a vital research topic, especially in the era of big data. In this abstract, we present our adaptive memetic algorithm for selection of refined (significantly smaller) SVM training sets. The algorithm - being a hybrid of an adaptive genetic algorithm and some refinement procedures - exploits the knowledge about the training set vectors extracted before the evolution, and attained dynamically during the search. The results obtained for several real-life, benchmark, and artificial datasets showed that our approach outperforms the other state-of-the-art techniques, and is able to extract very high-quality SVM training sets.
KW - Adaptation
KW - Memetic algorithm
KW - PCA
KW - SVM
KW - Training set selection
UR - https://www.scopus.com/pages/publications/84986308419
U2 - 10.1145/2908961.2930950
DO - 10.1145/2908961.2930950
M3 - Conference contribution
AN - SCOPUS:84986308419
T3 - GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
SP - 165
EP - 166
BT - GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
A2 - Friedrich, Tobias
PB - Association for Computing Machinery, Inc
Y2 - 20 July 2016 through 24 July 2016
ER -