Abstract
Relational classification is a promising branch of machine learning techniques for classification in networked environments which does not fulfil the iid assumption (independent and identically distributed). During the past few years, researchers have proposed many relational classification methods. However, almost none of them was able to work efficiently with large amounts of data or sparsely labelled networks. It is introduced in this paper a new approach to relational classification based on competence region modelling. The approach aims at solving large relational data classification problems, as well as seems to be a reasonable solution for classification of sparsely labelled networks by decomposing the initial problem to subproblems (competence regions) and solve them independently. According to preliminary results obtained from experiments performed on real world datasets competence region modelling approach to relational classification results with more accurate classification than standard approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chakrabarti, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pp. 307–319 (1998)
Taskar, B., Segal, E., Koller, D.: Probabilistic classification and clustering in relational data. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), pp. 870–878 (2001)
Neville, J., Jensen, D.: Collective classification with relational dependency networks. In: Proceedings of the Multi-Relational Data Mining Workshop (MRDM) at the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2003)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning (ICML), pp. 282–289 (2001)
Segal, E., Wang, H., Koller, D.: Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19, 264–272 (2003)
Segal, E., Yelensky, R., Koller, D.: Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics 19, 273–282 (2003)
Fawcett, T., Provost, F.: Adaptive fraud detection. Data Mining and Knowledge Discovery 3, 291–316 (1997)
Cortes, C., Pregibon, D., Volinsky, C.: Communities of Interest. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, p. 105. Springer, Heidelberg (2001)
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)
Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems (TOIS) 22, 116–142 (2004)
Macskassy, S.A., Provost, F.: Classification in networked data: A toolkit and a univariate case study. J. Mach. Learn. Res. 8, 935–983 (2007), http://portal.acm.org/citation.cfm?id=1248693
Neville, J., Gallagher, B., Eliassi-rad, T.: Evaluating statistical tests for Within-Network classifiers of relational data, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.8070
Almack, J.C.: The influence of intelligence on the selection of associates. School and Society 16, 529–530 (1922)
Bott, H.: Observation of play activities in a nursery school. Genetic Psychology Monographs 4, 44–88 (1928)
Richardson, H.M.: Community of values as a factor in friendships of college and adult women. Journal of Social Psychology 11, 303–312 (1940)
Loomis, C.P.: Political and occupational cleavages in a Hanoverian village. Sociometry 9, 316–3333 (1946)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27, 415–444 (2001)
Neville, J., Jensen, D.: Leveraging relational autocorrelation with latent group models. In: Proceedings of the 4th International Workshop on Multi-Relational Mining, MRDM 2005, pp. 49–55. ACM, New York (2005), http://dx.doi.org/10.1145/1090193.1090201
Nooy, W., Mrvar, A., Batagelj, V.: Exploratory Social Network Analysis with Pajek, ch. 11. Cambridge University Press (2004)
Newman, M.: Finding community structure in networks using the eigenvectors of matrices. Physical Review E 74, 36–104 (2006)
Musiał, K., Kazienko, P., Bródka, P.: User position measures in social networks. In: Proceedings of the 3rd Workshop on Social Network Mining and Analysis, SNA-KDD 2009 (2009)
Kuncheva, L.I.: Clustering-and-selection model for classifier combination. In: Proceedings of KES 2000 Fouth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, vol. 1, pp. 185–188 (2000)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley Interscience (2004)
Dzeroski, S., Lavrac, N.: Relational Data Mining. Springer, Berlin (2001)
Jensen, D., Neville, J.: Data mining in social networks. National Academy of Sciences workshop on Dynamic Social Network Modeling and Analysis (2002)
Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004)
Neville, J., Jensen, D.: Iterative classification in relational data. In: AAAI Workshop on Learning Statistical Models from Relational Data, pp. 13–20 (2000)
Scott, J.: Social network analysis: A handbook, 2nd edn. Sage Publications Ltd., London (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kajdanowicz, T., Filipowski, T., Kazienko, P., Bródka, P. (2013). Competence Region Modelling in Relational Classification. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36543-0_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-36543-0_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36542-3
Online ISBN: 978-3-642-36543-0
eBook Packages: Computer ScienceComputer Science (R0)