Abstract
This paper presents a new network-based classification technique using limiting probabilities from random walk theory. Instead of using a traditional heuristic to classify data relying on physical features such as similarity or density distribution, it uses a concept called ease of access. By means of an underlying network, in which nodes represent states for the random walk process, unlabeled instances are classified with the label of the most easily reached class. The limiting probabilities are used as a measure for the ease of access by taking into account the biases provided by an unlabeled instance in a specific adjacency matrix weight composition. In this way, the technique allows data classification from a different viewpoint. Simulation results suggest that the proposed scheme is competitive with current and well-known classification algorithms.
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References
Duda, R.O., Stork, D.G., Hart, P.E.: Pattern classification, 2nd edn. Wiley-Interscience (2000)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1998)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley (2005)
Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: Proceedings of the Eighteenth International Conference on Machine Learning, San Francisco, pp. 19–26. Morgan Kaufmann (2001)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proc. 20th Int. Conf. Mach. Learn., pp. 912–919 (2003)
Belkin, M., Matveeva, I., Niyogi, P.: Regularization and semi-supervised learning on large graphs. In: Shawe-Taylor, J., Singer, Y. (eds.) COLT 2004. LNCS (LNAI), vol. 3120, pp. 624–638. Springer, Heidelberg (2004)
Belkin, M., Niyogi, P., Sindhwani, V.: On manifold regularization. In: Proc. 10th Int. Workshop Artif. Intell. Stat., pp. 17–24 (2005)
Zhou, D., Schölkopf, B.: Adaptive computation and machine learning. In: Discrete Regularization, pp. 237–250. MIT Press, Cambridge (2006)
Silva, T., Zhao, L.: Network-based stochastic semisupervised learning. IEEE Transactions on Neural Networks and Learning Systems 23(3), 451–466 (2012)
Zheng, X., Lin, X.: Automatic determination of intrinsic cluster number family in spectral clustering using random walk on graph. In: 2004 International Conference on Image Processing, ICIP 2004, vol. 5, pp. 3471–3474 (October 2004)
Alamgir, M., von Luxburg, U.: Multi-agent random walks for local clustering on graphs. In: 2010 IEEE 10th International Conference on Data Mining, ICDM, pp. 18–27 (December 2010)
Cai, B., Wang, H., Zheng, H., Wang, H.: An improved random walk based clustering algorithm for community detection in complex networks. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC, pp. 2162–2167 (October 2011)
Breve, F., Zhao, L., Quiles, M., Pedrycz, W., Liu, J.: Particle competition and cooperation in networks for semi-supervised learning. IEEE Transactions on Knowledge and Data Engineering 24(9), 1686–1698 (2012)
Silva, T., Zhao, L.: Stochastic competitive learning in complex networks. IEEE Transactions on Neural Networks and Learning Systems 23(3), 385–398 (2012)
Bertini, J.R., Zhao, L., Motta, R., de Andrade Lopes, A.: A nonparametric classification method based on k-associated graphs. Information Sciences 181, 5435–5456 (2011)
Cupertino, T.H., Silva, T., Zhao, L.: Classification of multiple observation sets via network modularity. Neural Computing and Applications (Print) (2012)
Cupertino, T.H., Zhao, L.: Using katz centrality to classify multiple pattern transformations. In: Proceedings of the 2012 Brazilian Symposium on Neural Networks, pp. 1–6 (2012)
Silva, T., Zhao, L.: Network-based high level data classification. IEEE Transactions on Neural Networks and Learning Systems 23(6), 954–970 (2012)
Gallager, R.G.: Discrete Stochastic Processes, 1st edn. Springer (1996)
Frank, A., Asuncion, A.: UCI machine learning repository (2010)
Kim, J.H.: Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics and Data Analysis 53, 3735–3745 (2009)
Quinlan, J.R.: C4.5: Programs for Machine Learning, 1st edn. Morgan Kaufman Publishers (1993)
Vapnik, V.: The Nature of Statistical Learning Theory, 1st edn. Springer (1999)
Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)
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Cupertino, T.H., Zhao, L. (2013). Bias-Guided Random Walk for Network-Based Data Classification. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_46
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DOI: https://doi.org/10.1007/978-3-642-39068-5_46
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