Skip to main content
Erweiterte Suche
Anmelden
nach oben
Journal of Classification
Erschienen in:

21.01.2019

Quantum-Behaved Particle Swarm Optimization for Parameter Optimization of Support Vector Machine

verfasst von: Alaa Tharwat, Aboul Ella Hassanien

Erschienen in: Journal of Classification | Ausgabe 3/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Support vector machine (SVM) parameters such as penalty parameter and kernel parameters have a great influence on the complexity and accuracy of SVM model. In this paper, quantum-behaved particle swarm optimization (QPSO) has been employed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (QPSO-SVM), the experiment adopted seven standard classification datasets which are obtained from UCI machine learning data repository. For verification, the results of the QPSO-SVM algorithm are compared with the standard PSO, and genetic algorithm (GA) which is one of the well-known optimization algorithms. Moreover, the results of QPSO are compared with the grid search, which is a conventional method of searching parameter values. The experimental results demonstrated that the proposed model is capable to find the optimal values of the SVM parameters. The results also showed lower classification error rates compared with standard PSO and GA algorithms.
Vorheriger Artikel A Note on Applying the BCH Method Under Linear Equality and Inequality Constraints
Nächster Artikel MDCGen: Multidimensional Dataset Generator for Clustering

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
Ali, S., & Smith, K. (2003). Automatic parameter selection for polynomial kernel. In Proceedings of IEEE International Conference on Information Reuse and Integration, (IRI 2003), Lens, France, October, (Vol. 27-29 pp. 243–249).
Bashir, Z., & El-Hawary, M. (2009). Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Transactions on Power Systems, 24(1), 20–27.CrossRef
Ben-Hur, A., Ong, C.S., Sonnenburg, S., Schölkopf, B., Rätsch, G. (2008). Support vector machines and kernels for computational biology. PLoS Comput Biol, 4(10), e1000173.CrossRef
Blake, C., & Merz, C.J. (1998). {UCI} repository of machine learning databases repository of machine learning databases.
Byvatov, E., & Schneider, G. (2002). Support vector machine applications in bioinformatics. Applied Bioinformatics, 2(2), 67–77.
Cai, Y., Sun, J., Wang, J., Ding, Y., Tian, N., Liao, X., et al. (2008). Optimizing the codon usage of synthetic gene with QPSO algorithm. Journal of Theoretical Biology, 254(1), 123–127.MathSciNetMATHCrossRef
Chander, A., Chatterjee, A., Siarry, P. (2011). A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Systems with Applications, 38(5), 4998–5004.CrossRef
Chapelle, O., Vapnik, V., Bousquet, O. (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46(1-3), 131–159.MATHCrossRef
Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.CrossRef
Dudani, S.A. (1976). The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(4), 325–327.CrossRef
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995. MHS’95. pp. 39–43.
Friedrichs, F., & Igel, C. (2005). Evolutionary tuning of multiple SVM parameters. Neurocomputing, 64, 107–117.CrossRef
Hassan, R., Cohanim, B., De, Weck, Venter, O. (2005). G A comparison of particle swarm optimization and the genetic algorithm. In Proceedings of the 1st AIAA multidisciplinary design optimization specialist conference, Honolulu, Hawaii, April 23-26 (pp. 1–13).
He, H., & Garcia, E.A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.CrossRef
Huang, C.L., & Wang, C.J. (2006). A GA-based feature selection and parameters optimizationfor support vector machines. Expert Systems with Applications, 31(2), 231–240.CrossRef
Kecman, V. (2001). Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. Cambridge: MIT Press.MATH
Keerthi, S.S., & Lin, C.J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15(7), 1667–1689.MATHCrossRef
Kennedy, J. (2004). Probability and dynamics in the particle swarm. In Congress on Evolutionary Computation.
Kennedy, J. (2005). Dynamic-probabilistic particle swarms. In Proceedings of the 7th Annual Conference On Genetic And Evolutionary Computation, pp. 201–207.
Kennedy, J. (2010). Particle swarm optimization. In Encyclopedia of Machine Learning. Springer, pp. 760–766.
Krohling, R.A. (2004). Gaussian swarm: a novel particle swarm optimization algorithm. In Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, (Vol. 1 pp. 372–376).
Krohling, R.A., & Santos Coelho, L. dos. (2006). PSO-E: particle swarm with exponential distribution. In 2006 IEEE International Conference on Evolutionary Computation, pp 1428–1433.
Li, S., Wang, R., Hu, W., Sun, J. (2007). A new QPSO based BP neural network for face detection. In Fuzzy Information and Engineerin. Springer, pp. 355–363.
Liang, J.J., Qin, A.K., Suganthan, P.N. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.CrossRef
Lin, S.W., Ying, K.C., Chen, S.C., Lee, Z.J. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, 35(4), 1817–1824.CrossRef
Liu, B., Wang, L., Jin, Y.H., Tang, F. (2005). Huang D X. Chaos, Solitons & Fractals, 25(5), 1261–1271.CrossRef
Liu, J., Xu, W., Sun, J. (2005). Quantum-behaved particle swarm optimization with mutation operator. In Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05).
Maitra, M., & Chatterjee, A. (2008). A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Systems with Applications, 34(2), 1341– 1350.CrossRef
Merwe, D., Van der, Engelbrecht, A.P. (2003). Data clustering using particle swarm optimization. In The 2003 Congress on Evolutionary Computation(CEC’03), (Vol. 1 pp. 215–220).
Mikki, S.M., & Kishk, A.A. (2006). Quantum particle swarm optimization for electromagnetics. IEEE Transactions on Antennas and Propagation, 54(10), 2764–2775.CrossRef
Miyatake, M., Veerachary, M., Toriumi, F., Fujii, N., Ko, H. (2011). Maximum power point tracking of multiple photovoltaic arrays: a PSO approach. IEEE Transactions on Aerospace and Electronic Systems, 47(1), 367–380.CrossRef
Omkar, S., Khandelwal, R., Ananth, T., Naik, G.N. (2009). Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures. Expert Systems with Applications, 36(8), 11312–11322.CrossRef
Pal, S.K., & Mitra, S. (1992). Multilayer perceptron, fuzzy sets, and classification. IEEE Transactions on Neural Networks, 3(5), 683–697.CrossRef
Panda, S., & Padhy, N.P. (2008). Optimal location and controller design of STATCOM for power system stability improvement using PSO. Journal of the Franklin Institute, 345(2), 166–181.MATHCrossRef
Reynolds, C.W. (1987). Flocks, herds and schools: a distributed behavioral model. ACM Siggraph Computer Graphics, 21(4), 25–34.CrossRef
Richer, T.J., & Blackwell, T.M. (2006). The Lévy particle swarmvy particle swarm. In 2006 IEEE International Conference on Evolutionary Computation, pp. 808–815.
Santos Coelho, L. dos. (2010). Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Systems with Applications, 37(2), 1676–1683.CrossRef
Santos Coelho, L. dos, & Krohling, R.A. (2005). Predictive controller tuning using modified particle swarm optimization based on Cauchy and Gaussian distributions. In Soft Computing: Methodologies and Applications. Springer, pp. 287–298.
Scholköpf, B., & Smola, A.J. (2001). Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge: MIT Press.
Subasi, A. (2013). Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Computers in Biology and Medicine, 43(5), 576–586.CrossRef
Sun, J., Fang, W., Palade, V., Wu, X. (2011). Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point. Applied Mathematics and Computation, 218(7), 3763–3775.MATHCrossRef
Sun, J., Fang, W., Wu, X., Palade, V. (2012). Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection. Evolutionary Computation, 20(3), 349–393.CrossRef
Sun, J., & Feng, B. (2004). Particle swarm optimization with particles having quantum behavior. In Congress on Evolutionary Computation.
Sun, J., Xu, W., Feng, B. (2004). A global search strategy of quantum-behaved particle swarm optimization. In Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, (Vol. 1 pp. 111–116).
Sun, J., Xu, W., Feng, B. (2005). Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, (Vol. 4 pp. 3049–3054).
Tharwat, A. (2016). Linear vs. quadratic discriminant analysis classifier: a tutorial. International Journal of Applied Pattern Recognition, 3(2), 145–180.CrossRef
Tharwat, A. (2016). Principal component analysis-a tutorial. International Journal of Applied Pattern Recognition, 3(3), 197–240.CrossRef
Tharwat, A., Gabel, T., Hassanien, A.E. (2017). Parameter optimization of support vector machine using dragonfly algorithm. In International Conference on Advanced Intelligent Systems and Informatics, pp. 309–319.
Tharwat, A., Gaber, T., Ibrahim, A. (2017). Linear discriminant analysis: a detailed tutorial. AI Communications, 30(2), 169–190.MathSciNetCrossRef
Tharwat, A., & Hassanien, A.E. (2018). Chaotic antlion algorithm for parameter optimization of support vector machine. Applied Intelligence, 48(3), 670–686.CrossRef
Tharwat, A., Hassanien, A.E., Elnaghi, B.E. (2016). A BA-based algorithm for parameter optimization of support vector machine. Pattern Recognition Letters.
Tharwat, A., Hemedan, A.A., Hassanien, A.E., Thomas G. (2018). A biometric-based model for fish species classification. Fisheries Research, 204, 324–336.CrossRef
Tharwat, A., & Moemen, Y.S. (2017). Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines. Journal of Biomedical Informatics, 68, 132–149.CrossRef
Wang, G., & Guo, L. (2013). A novel hybrid bat algorithm with harmony search for global numerical optimization. Journal of Applied Mathematics.
Wang, L. (2005). Support vector machines: theory and applications, vol. 177. Berlin: Springer.CrossRef
Wu, C.H., Tzeng, G.H., Lin, R.H. (2009). A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Systems with Applications, 36(3), 4725–4735.CrossRef
Xi, M., & Sun, J. (2008). An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position. Applied Mathematics and Computation, 205(2), 751–759.MATHCrossRef
Xinchao, Z. (2010). A perturbed particle swarm algorithm for numerical optimization. Applied Soft Computing, 10(1), 119–124.CrossRef
Yang, X.S. (2014). Nature-inspired optimization algorithms, 1st edn. Amsterdam: Elsevier.MATH
Zhang, X., & Chen, X. (2010). An ACO-based algorithm for parameter optimization of support vector machines. Expert Systems with Applications, 37(9), 6618–6628.CrossRef
Zhang, Y., & Zhang, P. (2015). Machine training and parameter settings with social emotional optimization algorithm for support vector machine. Pattern Recognition Letters, 54, 36–42.CrossRef
Zhao, M., Fu, C., Ji, L., Tang, K. (2011). Feature selection and parameter optimization for support vector machines: a new approach based on genetic algorithm with feature chromosomes. Expert Systems with Applications, 38(5), 5197–5204.CrossRef
Metadaten
Titel
Quantum-Behaved Particle Swarm Optimization for Parameter Optimization of Support Vector Machine
verfasst von
Alaa Tharwat
Aboul Ella Hassanien
Publikationsdatum
21.01.2019
Verlag
Springer US
Erschienen in
Journal of Classification / Ausgabe 3/2019
Print ISSN: 0176-4268
Elektronische ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-018-9299-1

Weitere Artikel der Ausgabe 3/2019

Three-Way Symbolic Tree-Maps and Ultrametrics

  • Open Access

A Framework for Analyzing Pairwise Experiments with Qualitative Responses

Card Sorting Data Collection Methodology: How Many Participants Is Most Efficient?

Comparing the Utility of Different Classification Schemes for Emotive Language Analysis

  • Open Access

Editorial: Journal of Classification Vol. 36-3

MCC: a Multiple Consensus Clustering Framework

Premium Partner

    Bildnachweise