Abstract
Weighted combination model with appropriate weight vector is very effective in multiple classifier systems. We presented a method for determining the weight vector by particle swarm optimization in our previous work, which called PSO-WCM. A weighted combination model, PSO-LS-WCM, was proposed in this paper to improve the classification performance further, which obtained the weighted vector by particle swarm optimization with local search. We describe the algorithm of PSO-LS-WCM in detail. Seven real-world problems from UCI Machine Learning Repository were used in experiments to justify the validity of the approach. It was shown that PSO-LS-WCM is better than PSO-WCM and the other six combination methods in literature.
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References
Hansen, L., Salamon, P.: Neural Network Ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)
Brown, G.: Ensemble Learning. In: Encyclopedia of Machine Learning. Springer Press, Heidelberg (2010)
Suen, C.Y., Lam, L.: Multiple classifier combination methodologies for different output levels. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 52–66. Springer, Heidelberg (2000)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc., Chichester (2004)
Yang, L.-y., Qin, Z.: Combining Classifiers with Particle Swarms. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 756–763. Springer, Heidelberg (2005)
Yang, L., Zhang, J., Wang, W.: Selecting and Combining Classifiers Simultaneously with Particle Swarm Optimization. Information Technology Journal 8(2), 241–245 (2009)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)
Eberhart, R., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proceeding of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Angeline, P.J.: Ebulutionary optimization versus particle swarm optimization: philosophy and performance differences. Evolutionary programming VII. In: Proceedings of the Seventh Annual Conference on Evolutionary Programming (1998)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE World Congress on Computational Intelligence, pp. 69–73 (1998)
Clerc, M.: The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization. In: Proceeding of the Congress of Evolutionary Computation, vol. 3, pp. 1951–1957 (1999)
Chen, J., Qin, Z., Liu, Y., Lu, J.: Particle Swarm Optimization with Local Search. In: Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB 2005, pp. 481–484 (2005)
Tomas, A.: Constraints in Weighted Averaging. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 354–363. Springer, Heidelberg (2009)
Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases (1998), www.ics.uci.edu/~mlearn/MLRepository.html
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions On Pattern Analysis and Machine Intelligence 3, 226–239 (1998)
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Yang, L. (2011). Combining Classifiers by Particle Swarms with Local Search. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_29
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DOI: https://doi.org/10.1007/978-3-642-21524-7_29
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