Abstract
It is common that different people share the same name. When it occurs in bibliography databases, it worsens the performance of information retrieval and data management. In this paper, we address the problem of name disambiguation and propose two different strategies, one classifier for each name (OCEN) and one classifier for all names (OCAN). Both strategies OCEN and OCAN are based on extreme learning machine (ELM) which shows similar or better generalization performance and faster learning speed than support vector machines (SVM) and least squares support vector machines (LS-SVM). We conduct experiments to compare the performance of ELM, SVM and LS-SVM in the two strategies.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Canu, S., Grandvalet, Y., Guigue, V., Rakotomamonjy, A.: SVM and Kernel Methods Matlab Toolbox. Published: Perception Systmes et Information, INSA de Rouen, Rouen, France (2005)
Chacko, B.P., Vimal Krishnan, V.R., Raju, G., Babu Anto, P.: Handwritten character recognition using wavelet energy and extreme learning machine. Int. J. Mach. Learn. Cybern. 3(2), 149–161 (2012)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
DBLP bibliography: http://www.informatik.uni-trier.de/ ley/db/. Accessed May 2012
The Porter stemming algorithm: http://snowball.tartarus.org/algorithms/porter/stemmer.html. Accessed July 1980
Fan, X., Wang, J., Pu, X., Zhou, L., Lv, B.: On graph-based name disambiguation. J. Data Inf. Qual. 2(2), 5–16 (2011)
Feng, G., Huang, G., Lin, Q., Gay, R.: Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans. Neural Networ. 20(8), 1352–1357 (2009)
Han, H., Giles, L., Zha, H., Li, C., Tsioutsiouliklis, K.: Two supervised learning approaches for name disambiguation in author citations. In: Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries; Global Reach and Diverse Impact, JCDL 2004, 7–11 June 2004, pp. 296–305. Tucson, AZ, United States (2004)
Han, H., Zha, H., Giles, C.: Name disambiguation in author citations using a k-way spectral clustering method. In: 5th ACM/IEEE Joint Conference on Digital Libraries—Digital Libraries: Cyberinfrastructure for Research and Education, 7–11 June 2005, pp. 334–343. Denver, CO, United States (2005)
Huang, G., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70(16–18), 3056–3062 (2007)
Huang, G., Chen, L.: Enhanced random search based incremental extreme learning machine. In: Advances in Neural Information Processing (ICONIP 2006)/Brazilian Symposium on Neural Networks (SBRN 2006). Neurocomputing, vol. 71, pp. 3460–3468. Elsevier, Amsterdam (2008)
Huang, G., Slew, C.: Extreme learning machine: RBF network case. In: 8th International Conference on Control, Automation, Robotics and Vision (ICARCV), 6–9 Dec 2004. 2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV), vol. 2, pp. 1029–1036. Institute of Electrical and Electronics Engineers Inc., Kunming (2004)
Huang, G., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2, 107–122 (2011)
Huang, G., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. B 42(2), 513–529 (2012)
Huang, G., Zhu, Q., Siew, C.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Lan, Y., Soh, Y.C., Huang, G.: Ensemble of online sequential extreme learning machine. Neurocomputing 72(13–15), 3391–3395 (2009)
Liang, N., Huang, G., Saratchandran, P., Sundararajan, N.: A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. (A Publication of the IEEE Neural Networks Council) 17, 1411–1423 (2006)
LS-SVMlab: http://www.esat.kuleuven.be/sista/lssvmlab/. Accessed 16 Aug 2011
Mohammed, A.A., Minhas, R., JonathanWu, Q.M., Sid-Ahmed, M.A.: Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recogn. 44(10–11), 2588–2597 (2011)
Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7(4), 308–313 (1965)
Smith, L.I.: A Tutorial on Principal Components Analysis, vol. 51, no. 52. Cornell University, USA (2002)
Sun, Y., Yuan, Y., Wang, G.: An os-elm based distributed ensemble classification framework in P2P networks. Neurocomputing 74(16), 2438–2443 (2011)
Suykens, J., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Wang, G., Zhao, Y., Wang, D.: A protein secondary structure prediction framework based on the extreme learning machine. Neurocomputing 72(1–3), 262–268 (2008)
Zhai, J.H., Xu, H.Y., Wang, X.Z.: Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput. 16(9), 1493–1502 (2012)
Zhao, X., Wang, G., Bi, X., Gong, P., Zhao, Y.: Xml document classification based on ELM. Neurocomputing 74(16), 2444–2451 (2011)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Han, D., Liu, S., Hu, Y. et al. ELM-based name disambiguation in bibliography. World Wide Web 18, 253–263 (2015). https://doi.org/10.1007/s11280-013-0226-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11280-013-0226-4