TY - GEN
T1 - Support vector machines training data selection using a genetic algorithm
AU - Kawulok, Michal
AU - Nalepa, Jakub
PY - 2012
Y1 - 2012
N2 - This paper presents a new method for selecting valuable training data for support vector machines (SVM) from large, noisy sets using a genetic algorithm (GA). SVM training data selection is a known, however not extensively investigated problem. The existing methods rely mainly on analyzing the geometric properties of the data or adapt a randomized selection, and to the best of our knowledge, GA-based approaches have not been applied for this purpose yet. Our work was inspired by the problems encountered when using SVM for skin segmentation. Due to a very large set size, the existing methods are too time-consuming, and random selection is not effective because of the set noisiness. In the work reported here we demonstrate how a GA can be used to optimize the training set, and we present extensive experimental results which confirm that the new method is highly effective for real-world data.
AB - This paper presents a new method for selecting valuable training data for support vector machines (SVM) from large, noisy sets using a genetic algorithm (GA). SVM training data selection is a known, however not extensively investigated problem. The existing methods rely mainly on analyzing the geometric properties of the data or adapt a randomized selection, and to the best of our knowledge, GA-based approaches have not been applied for this purpose yet. Our work was inspired by the problems encountered when using SVM for skin segmentation. Due to a very large set size, the existing methods are too time-consuming, and random selection is not effective because of the set noisiness. In the work reported here we demonstrate how a GA can be used to optimize the training set, and we present extensive experimental results which confirm that the new method is highly effective for real-world data.
UR - https://www.scopus.com/pages/publications/84868122217
U2 - 10.1007/978-3-642-34166-3_61
DO - 10.1007/978-3-642-34166-3_61
M3 - Conference contribution
AN - SCOPUS:84868122217
SN - 9783642341656
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 557
EP - 565
BT - Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR and SPR 2012, Proceedings
T2 - Joint IAPR International Workshops on Structural and Syntactic PatternRecognition, SSPR 2012 and Statistical Techniques in Pattern Recognition,SPR 2012
Y2 - 7 November 2012 through 9 November 2012
ER -