TY - GEN
T1 - Tuning and evolving support vector machine models
AU - Nalepa, Jakub
AU - Kawulok, Michal
AU - Dudzik, Wojciech
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG.
PY - 2018
Y1 - 2018
N2 - Support vector machines (SVMs) are a well-established classifier, already applied in a variety of pattern recognition tasks. However, they suffer from several drawbacks—selecting their appropriate hyper-parameter values (the SVM model) along with the training sets being the most important. In this paper, we study the influence of applying various kernel functions in SVMs. We verify not only the classification performance of the classifier, but also the number of selected support vectors and the training time for each kernel. Also, we perform the qualitative analysis of the retrieved support vectors using an artificially generated dataset. Finally, we show how to optimize the SVM models using a genetic algorithm. An extensive experimental study revealed that evolved SVM models provide high-quality classification and are retrieved in much shorter time compared with the trial-and-error approaches.
AB - Support vector machines (SVMs) are a well-established classifier, already applied in a variety of pattern recognition tasks. However, they suffer from several drawbacks—selecting their appropriate hyper-parameter values (the SVM model) along with the training sets being the most important. In this paper, we study the influence of applying various kernel functions in SVMs. We verify not only the classification performance of the classifier, but also the number of selected support vectors and the training time for each kernel. Also, we perform the qualitative analysis of the retrieved support vectors using an artificially generated dataset. Finally, we show how to optimize the SVM models using a genetic algorithm. An extensive experimental study revealed that evolved SVM models provide high-quality classification and are retrieved in much shorter time compared with the trial-and-error approaches.
KW - Classification
KW - Genetic algorithm
KW - Hyper-parameters
KW - Kernel function
KW - Support vector machine
UR - https://www.scopus.com/pages/publications/85030754969
U2 - 10.1007/978-3-319-67792-7_41
DO - 10.1007/978-3-319-67792-7_41
M3 - Conference contribution
AN - SCOPUS:85030754969
SN - 9783319677910
T3 - Advances in Intelligent Systems and Computing
SP - 418
EP - 428
BT - Man-Machine Interactions 5 - 5th International Conference on Man-Machine Interactions, ICMMI 2017
A2 - Gruca, Aleksandra
A2 - Czachorski, Tadeusz
A2 - Harezlak, Katarzyna
A2 - Kozielski, Stanislaw
A2 - Piotrowska, Agnieszka
A2 - Czachorski, Tadeusz
PB - Springer Verlag
T2 - 5th International Conference on Man-Machine Interactions, ICMMI 2017
Y2 - 3 October 2017 through 6 October 2017
ER -