Abstract
In Dynamic Classifier Selection (DCS) techniques, test sample is classified only by the most competent classifiers. Hence, the major problem in DCS is to find the measures by which competence of classifiers in a pool can be calculated to find out the most competent classifiers. To tackle these issues, we suggest a Framework for Dynamic Ensemble Selection (DES) that uses more than one criterion to calculate the base classifier’s competence level. The framework has three major steps. In first step, training data is used to create a pool consisting of different classifiers. In second step meta-classifier training is performed by extracting meta-features from training data. In third step meta-classifier uses meta-features extracted from test sample to perform an ensemble selection and to predict the final output. In this paper, we suggest some improvements in second step (training) and last step (generalization) of the framework. In training phase, four different models are used as meta-classifiers. While in generalization phase, dynamic weighting scheme is used where meta-classifiers will dynamically assign weights to selected competent classifiers based on their competence level and final decision will be aggregated using a weighting voting scheme. The modifications purposed in this paper altogether enhance performance and accuracy of the framework in contrast with other dynamic selection techniques in literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Woźniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014)
Britto Jr., A.S., Sabourin, R., Oliveira, L.E.: Dynamic selection of classifiers as a comprehensive review. Pattern Recognit. 47(11), 3665–3680 (2014)
Ko, A.H.R., Sabourin, R., Britto Jr., A.S.: From dynamic classifiers election to dynamic ensemble selection. Pattern Recognit. 41, 1735–1748 (2008)
Caval, P.R., Sabourin, R., Suen, C.Y.: Dynamic selection approaches for multiple classifier systems. Neural Comput. Appl. 22(3–4), 673–688 (2013)
Cavalin, P.R., Sabourin, R., Suen, C.Y.: LoGID: an adaptive framework combining local and global incremental learning for dynamic selection of ensembles of HMMs. Pattern Recognit. 45(9), 3544–3556 (2012)
Woloszynski, T., Kurzynski, M., Podsiadlo, P., Stachowiak, G.W.: A measure of competence based on random classification for dynamic ensemble selection. Inf. Fusion 13(3), 207–213 (2012)
Cruz, R.M.O., Sabourin, R., Cavalcanti, G.D.C.: Prototype selection for dynamic classifier and ensemble selection. Neural Comput. Appl. 29(2), 447–457 (2018)
Mejri, D., Limam, M., Weihs, C.: A new dynamic weighted majority control chart for data streams. Soft Comput. 22(2), 511–522 (2018)
Xiao, H., Xiao, Z., Wang, Y.: Ensemble classification based on supervised clustering for credit scoring. Appl. Soft Comput. 43, 73–86 (2016)
Brun, A.L., Britto Jr., A.S., Oliveira, L.S., Enembreck, F., Sabourin, R.: A framework for dynamic classifier selection oriented by the classification problem difficulty. Pattern Recognit. 76, 175–190 (2018)
Xia, Y., Liu, C., Da, B., Xie, F.: A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Syst. Appl. 93, 182–199 (2018)
Liu, X., Li, Q., Li, T., Chen, D.: Differentially private classification with decision tree ensemble. Appl. Soft Comput. 62, 807–816 (2018)
van Rijn, J.N., Holmes, G., Pfahringer, B., Vanschoren, J.: The online performance estimation framework: heterogeneous ensemble learning for data streams. Mach. Learn. 107, 1–28 (2018)
Cruz, R.M.O., Robert, S., Cavalcanti, G.D.C.: META-DES. Oracle: meta-learning and feature selection for dynamic ensemble selection. Inf. Fusion 38, 84–103 (2017)
Oliveira, D.V.R., Cavalcanti, G.D.C., Sabourin, R.: Online pruning of base classifiers for Dynamic Ensemble Selection. Pattern Recognit. 72, 44–58 (2017)
Ye, R., Dai, Q.: A novel greedy randomized dynamic ensemble selection algorithm. Neural Process. Lett. 47(2), 565–599 (2018)
Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J., Woźniak, M.: Ensemble learning for data stream analysis: a survey. Inf. Fusion 37, 132–156 (2017)
Pérez-Gállego, P., Castaño, A., Quevedo, J.R., del Coz, J.J.: Dynamic ensemble selection for quantification tasks. Inf. Fusion 45, 1–15 (2018)
Roy, A., Cruz, R.M.O., Sabourin, R., Cavalcanti, G.D.C.: A study on combining dynamic selection and data preprocessing for imbalance learning. Neurocomputing 286, 179–192 (2018)
García, S., Zhang, Z.-L., Altalhi, A., Alshomrani, S., Herrera, F.: Dynamic ensemble selection for multi-class imbalanced datasets. Inf. Sci. 445, 22–37 (2018)
Cheriguene, S., Azizi, N., Dey, N., Ashour, A.S., Ziani, A.: A new hybrid classifier selection model based on mRMR method and diversity measures. Int. J. Mach. Learn. Cybern. 10, 1–16 (2018)
Pratama, M., Pedrycz, W., Lughofer, E.: Evolving ensemble fuzzy classifier. IEEE Trans. Fuzzy Syst. 26, 2552–2567 (2018)
Khamar, M., Eftekhari, M.: Multi-Manifold based Rotation Forest for classification. Appl. Soft Comput. 68, 626–635 (2018)
Almeida, P.R.L., Oliveira, L.S., Britto Jr., A.S., Sabourin, R.: Adapting dynamic classifier selection for concept drift. Expert. Syst. Appl. 104, 67–85 (2018)
Feng, X., Xiao, Z., Zhong, B., Qiu, J., Dong, Y.: Dynamic ensemble classification for credit scoring using soft probability. Appl. Soft Comput. 65(C), 139–151 (2018)
Cruz, R.M., Sabourin, R., Cava1canti, G.D., Ren, T.l.: META-DES: a dynamic ensemble selection framework using meta-learning. Pattern Recognit. 48(5), 1925–1935 (2015)
Breiman, L.: Bagging predictors. Machine Learn. 24, 123–140 (1996)
Fernandez-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15, 3133–3181 (2014). [Online] Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) CONFERENCE 2016, LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Qadeer, A., Qamar, U. (2020). A Dynamic Ensemble Selection Framework Using Dynamic Weighting Approach. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-29516-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29515-8
Online ISBN: 978-3-030-29516-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)