Efficient support vector regression with reduced training data

• Autorzy: Cen Ling , Vu Quang Hieu , Ruta Dymitr

Identyfikatory

DOI

10.15439/2019F362

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

Support Vector Regression (SVR) as a supervised machine learning algorithm have gained popularity in various fields. However, the quadratic complexity of the SVR in the number of training examples prevents it from many practical applications with large training datasets. This paper aims to explore efficient ways that maximize prediction accuracy of the SVR at the minimum number of training examples. For this purpose, a clustered greedy strategy and a Genetic Algorithm (GA) based approach are proposed for optimal subset selection. The performance of the developed methods has been illustrated in the context of Clash Royale Challenge 2019, concerned with decks' win rate prediction. The training dataset with 100,000 examples were reduced to hundreds, which were fed to SVR training to maximize model prediction performance measured in validation R2 score. Our approach achieved the second highest score among over hundred participating teams in this challenge.

Słowa kluczowe

EN

support vector regression; SVR; k-means clustering; greedy search; r-squared metric; Clash Royale

PL

regresja wektorów nośnych; SVR; algorytm centroidów; gra komputerowa; Clash Royale

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 15--18
• Opis fizyczny: Bibliogr. 10 poz., wz., rys.

Twórcy

autor

Cen Ling

• EBTIC, Khalifa University, UAE

autor

Vu Quang Hieu

• Zalora, Singapore

autor

Ruta Dymitr

• EBTIC, Khalifa University, UAE

Bibliografia

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• 5. X. Xia,M. Lyu, T. Lok, G. Huang, "Methods of Decreasing the Number of Support Vectors via k-Mean Clustering," Proc. Int. Conf. Intelligent Computing, pp. 717-726, 2005.
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• 7. E. Osuna and F. Girosi, "Reducing the run-time complexity of support vector machines," Int. Conf. Pattern Recognition, Australia, 1998.
• 8. D. Geebelen, J. Suykens, J. Vandewalle, "Reducing the number of support vectors of SVM classifiers using the smoothed separable case approximation," IEEE Trans Neural Netw Learn Syst., vol. 23, no. 4, pp. 682-688, 2012.
• 9. T. Downs, K. Gates, and A. Masters, "Exact simplification of support vector solutions," Journal of Machine Learning Reseaerch, vol. 1, pp. 293-297, 2001.
• 10. G. Bakir, J. Weston, and L. Bottou, "Breaking SVM complexity with cross-training," Advances in Neural Information Processing Systems, vol. 17, pp. 81-88, 2005.

Uwagi

1. Track 1: Artificial Intelligence and Applications

2. Technical Session: 14th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).