Abstract
Ensemble clustering generates data partitions by using different data representations and/or clustering algorithms. Each partition provides independent evidence to generate the final partition: two instances falling in the same cluster provide evidence towards them belonging to the same final partition.
In this paper we argue that, for some data representations, the fact that two instances fall in the same cluster of a given partition could provide little to no evidence towards them belonging to the same final partition. However, the fact that they fall in different clusters could provide strong negative evidence of them belonging to the same partition.
Based on this concept, we have developed a new ensemble clustering algorithm which has been applied to the heartbeat clustering problem. By taking advantage of the negative evidence we have decreased the misclassification rate over the MIT-BIH database, the gold standard test for this problem, from 2.25 % to 1.45 %.
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References
Bakker, B., Heskes, T.: Clustering ensembles of neural network models. Neural Netw. 16(2), 261–269 (2003)
Blanco-Velasco, M., Weng, B., Barner, K.E.: ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 38(1), 1–13 (2008)
Celebi, M.E.: Partitional Clustering Algorithms. Springer, Cham (2014)
de Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206 (2004)
de Chazal, P., Reilly, R.B.: A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 53(12 Pt 1), 2535–2543 (2006)
Dudoit, S., Fridlyand, J.: Bagging to improve the accuracy of a clustering procedure. Bioinformatics 19(9), 1090–1099 (2003)
Fred, A.: Finding Consistent Clusters in Data Partitions. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 309–318. Springer, Heidelberg (2001)
Fred, A.L., Jain, A.K.: Combining multiple clusterings using evidence accumulation. IEEE Trans. Pattern Analy. Mach. Intell. 27(6), 835–850 (2005)
García, C.A., Otero, A., Vila, X., Márquez, D.G.: A new algorithm for wavelet-based heart rate variability analysis. Biomed. Signal Proces. Control 8(6), 542–550 (2013)
Gil, A., Caffarena, G., Márquez, D.G., Otero, A.: Hermite Polynomial Characterization of Heartbeats with Graphics Processing Units. In: IWBBIO 2014 (2014)
Hong, Y., Kwong, S., Chang, Y., Ren, Q.: Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm. Pattern Recogn. 41(9), 2742–2756 (2008)
Jane, R., Olmos, S., Laguna, P.: Adaptive Hermite models for ECG data compression: performance and evaluation with automatic wave detection. In: Computers in Cardiology (1993)
Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., Sörnmo, L.: Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans. Biomed. Eng. 47(7), 838–848 (2000)
Madeiro, J.P., Cortez, P.C., Oliveira, F.I., Siqueira, R.S.: A new approach to QRS segmentation based on wavelet bases and adaptive threshold technique. Med. Eng. Phys. 29(1), 26–37 (2007)
Márquez, D.G., Otero, A., Félix, P., García, C.A.: On the Accuracy of Representing Heartbeats with Hermite Basis Functions. In: BIOSIGNALS 2013, pp. 338–341 (2013)
Mathers, C.D., Loncar, D.: Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 3(11), e442 (2006)
Moody, G., Mark, R.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)
Park, K., Cho, B., Lee, D., Song, S., Lee, J., Chee, Y., Kim, I., Kim, S.: Hierarchical support vector machine based heartbeat classification using higher order statistics and Hermite basis function. In: 2008 Computers in Cardiology, pp. 229–232. IEEE, September 2008
Rodríguez-Fdez, I., Canosa, A., Mucientes, M., Bugarín, A.: STAC: a web platform for the comparison of algorithms using statistical tests (2015). http://tec.citius.usc.es/stac
Sokal, R.R.: A statistical method for evaluating systematic relationships. Univ. Kans. Sci. Bull. 38, 1409–1438 (1958)
Sörnmo, L., Laguna, P.: Bioelectrical signal processing in cardiac and neurological applications. Elsevier Academic Press, New York (2005)
Topchy, A.P., Jain, A.K., Punch, W.F.: A mixture model for clustering ensembles. In: SDM, pp. 379–390. SIAM (2004)
Young, T.Y., Huggins, W.H.: On the representation of electrocardiograms. IEEE Trans. Bio-Med. Electron. 10(3), 86–95 (1963)
Zhang, Z., Dong, J., Luo, X., Choi, K.S., Wu, X.: Heartbeat classification using disease-specific feature selection. Comput. Biol. Med. 46, 79–89 (2014)
Acknowledgments
This work was supported by the University San Pablo CEU under the grant PPC12/2014. David G. Márquez is funded by an FPU Grant from the Spanish Ministry of Education (MEC) (Ref. AP2012-5053). Constantino A. García acknowledges the support of Xunta de Galicia under “Plan I2C” Grant program (partially cofunded by The European Social Fund of the European Union).
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Márquez, D.G., Fred, A.L.N., Otero, A., García, C.A., Félix, P. (2015). Introducing Negative Evidence in Ensemble Clustering Application in Automatic ECG Analysis. In: Feragen, A., Pelillo, M., Loog, M. (eds) Similarity-Based Pattern Recognition. SIMBAD 2015. Lecture Notes in Computer Science(), vol 9370. Springer, Cham. https://doi.org/10.1007/978-3-319-24261-3_5
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DOI: https://doi.org/10.1007/978-3-319-24261-3_5
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