Abstract
Ensembles of classifiers are usually considered a valuable approach in different scenarios. A broad range of methods to deal with the construction, diversity and combination of multiple predictive models have been extensively studied. While the focus is often to obtain more accurate and robust predictions than single models seldom the individual contribution of classifiers which could contribute to a better understanding of the uncertainty associated with ensembles’ outputs is taken into account. In this work we look into this issue and focus on evaluating the individual ensemble classifier contributions using several scenarios. We propose a visual web model that allows for the evaluation of both individual contributions as well as their interactions. We apply the proposed approach on a benchmark dataset and show how it can visually be used to better understand the uncertainty associated with the construction of ensembles, presenting some insight on the individual contributions and interactions.
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References
Liu, L., Wang, L.: What has my classifier learned? visualizing the classification rules of bag-of-feature model by support region detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3586–3593 (2012)
Keim, D.: Information visualization and visual data mining. IEEE Trans. Visual Comput. Graphics 8(1), 1–8 (2002)
Wehrend, S., Lewis, C.: A problem-oriented classification of visualization techniques. In: IEEE Visualization, pp. 139–143 (1990)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67 (1997)
Silva, C., Lotrič, U., Ribeiro, B., Dobnikar, A.: Distributed text classification with an ensemble kernel-based learning approach. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40, 287–297 (2010)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1998)
Huehn, J.C., Huellermeier, E.: FURIA: an algorithm for unordered fuzzy rule induction. In: Hühn, J., Hüllermeier, E. (eds.) Data Mining and Knowledge Discovery, vol. 19, 3rd edn, pp. 293–319. Springer, Heidelberg (2009)
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Upper Saddle River (2003)
Salgado, C., Vieira, S.M., Mendonça, L.F., Finkelstein, S., Sousa, J.M.C.: Ensemble fuzzy models in personalized medicine: application to vasopressors administration. Eng. Appl. Artif. Intell. 49, 141–148 (2016)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)
Kuncheva, L.: Combining Pattern Classifiers - Methods and Algorithms. Wiley, Hoboken (2004)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Silva, C., Ribeiro, B. (eds.): Inductive Inference for Large Scale Text Classification, vol. 225. Springer, Heidelberg (2010)
Silva, C., Ribeiro, B.: Background on text classification. In: Inductive Inference for Large Scale Text Classification. Studies in Computational Intelligence, vol. 255. Springer (2010)
Gacquer D., Piechowiak S., Delmotte, F., Delcroix, V.: A genetic approach for training diverse classifier ensembles. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU, pp. 798–805 (2008)
Rehm, F., Klawonn, F., Kruse, R.: Rule classification visualization of high-dimensional data. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU, pp. 1944–1948 (2006)
Harle, C., Neill, D., Padman, R.: An information visualization approach to classification and assessment of diabetes risk in primary care. In: Li, J., Aleman, D., Sikora, R. (eds.) Proceedings of the 3rd INFORMS Workshop on Data Mining and Health Informatics (DM-HI 2008) (2008)
Talbot, J., Lee, B., Kapoor, A., Tan, D.: EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers. In: ACM CHI (2009)
Wang, J., Yu, B., Gasser, L.: Classification Visualization with Shaded Similarity Matrix. University of Illinois at Urbana-Campaign, Technical report GSLIS (2002)
Velu, C.M., Kashwan, K.R.: Performance Analysis for visual data mining classification techniques of decision tree, ensemble and SOM. Int. J. Comput. Appl. 57(22) (2012)
Holten, D.: Hierarchical edge bundles: visualization of adjacencyrelations in hierarchical data. IEEE Trans. Visualizationand Comput. Graphics 12(5), 741–748 (2006)
Keim, D.A., Mansmann, F., Schneidewind, J., Thomas, J., Ziegler, H.: Visual analytics: scope and challenges. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds.) Visual Data Mining. LNCS, vol. 4404, pp. 76–90. Springer, Heidelberg (2008)
Keim, D., Qu, H., Ma, K.-L.: Big-data visualization. IEEE Comput. Graphics Appl. 33(4), 20–21 (2013)
van Rijsbergen, C.: Information Retrieval, Butterworths (ed.) (1979)
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Silva, C., Ribeiro, B. (2016). Visualization of Individual Ensemble Classifier Contributions. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-40581-0_51
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DOI: https://doi.org/10.1007/978-3-319-40581-0_51
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