TY - GEN
T1 - Adaptive genetic algorithm to select training data for support vector machines
AU - Nalepa, Jakub
AU - Kawulok, Michal
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2014.
PY - 2014
Y1 - 2014
N2 - This paper presents a new adaptive genetic algorithm (AGA) to select training data for support vector machines (SVMs). SVM training data selection strongly influences the classification accuracy and time, especially in the case of large and noisy data sets. In the proposed AGA, a population of solutions evolves with time. The AGA parameters, including the chromosome length, are adapted according to the current state of exploring the solution space. We propose a new multi-parent crossover operator for an efficient search. A new metric of distance between individuals is introduced and applied in the AGA. It is based on the fast analysis of the vectors distribution in the feature space obtained using principal component analysis. An extensive experimental study performed on the well-known benchmark sets along with the real-world and artificial data sets, confirms that the AGA outperforms a standard GA in terms of the convergence capabilities. Also, it reduces the number of support vectors and allows for faster SVM classification.
AB - This paper presents a new adaptive genetic algorithm (AGA) to select training data for support vector machines (SVMs). SVM training data selection strongly influences the classification accuracy and time, especially in the case of large and noisy data sets. In the proposed AGA, a population of solutions evolves with time. The AGA parameters, including the chromosome length, are adapted according to the current state of exploring the solution space. We propose a new multi-parent crossover operator for an efficient search. A new metric of distance between individuals is introduced and applied in the AGA. It is based on the fast analysis of the vectors distribution in the feature space obtained using principal component analysis. An extensive experimental study performed on the well-known benchmark sets along with the real-world and artificial data sets, confirms that the AGA outperforms a standard GA in terms of the convergence capabilities. Also, it reduces the number of support vectors and allows for faster SVM classification.
KW - Adaptive genetic algorithm
KW - Support vector machines
KW - Training data selection
UR - https://www.scopus.com/pages/publications/84915754463
U2 - 10.1007/978-3-662-45523-4_42
DO - 10.1007/978-3-662-45523-4_42
M3 - Conference contribution
AN - SCOPUS:84915754463
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 514
EP - 525
BT - Applications of Evolutionary Computation - 17th European Conference, EvoApplications 2014, Revised Selected Papers
A2 - Esparcia-Alcázar, Anna I.
PB - Springer Verlag
T2 - 17th European Conference on Applications of Evolutionary Computation, EvoApplications 2014
Y2 - 23 April 2014 through 25 April 2014
ER -