Definition
Many real-world classification problems can be best described as a set of objects interconnected via links to form a network structure. The links in the network denote relationships among the instances such that the class labels of the instances are often correlated. Thus, knowledge of the correct label for one instance improves our knowledge about the correct assignments to the other instances it connects to. The goal of collective classification is to jointly determine the correct label assignments of all the objects in the network.
Motivation and Background
Traditionally, a major focus of machine learning is to solve classification problems: given a corpus of documents, classify each according to its topic label; given a collection of e-mails, determine which are spam; given a sentence, determine the part-of-speech tag for each word; given a handwritten document, determine the characters, etc. However, much of...
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Recommended Reading
Anguelov D, Taskar B, Chatalbashev V, Koller D, Gupta D, Heitz G et al (2005) Discriminative learning of Markov random fields for segmentation of 3D scan data. In: IEEE computer society conference on computer vision and pattern recognition, San Diego. IEEE Computer Society, Washington, DC
Berrou C, Glavieux A, Thitimajshima P (1993) Near Shannon limit error-correcting coding and decoding: Turbo codes. In: Proceedings of IEEE international communications conference, Geneva. IEEE
Besag J (1986) On the statistical analysis of dirty pictures. J R Stat Soc B-48:259–302
Carvalho V, Cohen WW (2005) On the collective of email speech acts. In: Special interest group on information retrieval, Salvador. ACM
Chakrabarti S, Dom B, Indyk P (1998) Enhanced hypertext categorization using hyperlinks. In: International conference on management of data, Seattle. ACM, New York
Chen L, Wainwright M, Cetin M, Willsky A (2003) Multitarget multisensor data association using the tree-reweighted max-product algorithm. In: SPIE Aerosense conference, Orlando
Getoor L (2005) Link-based . In: Advanced methods for knowledge discovery from complex data. Springer, New York
Getoor L, Taskar B (eds) (2007) Introduction to statistical relational learning. MIT, Cambridge
Getoor L, Segal E, Taskar B, Koller D (2001) Probabilistic models of text and link structure for hypertext . In: Proceedings of the IJCAI workshop on text learning: beyond supervision, Seattle
Getoor L, Friedman N, Koller D, Taskar B (2002) Learning probabilistic models of link structure. J Mach Learn Res 3:679–707
Hummel R, Zucker S (1983) On the foundations of relaxation labeling processes. IEEE Trans Pattern Anal Mach Intell 5:267–287
Jensen D, Neville J, Gallagher B (2004) Why collective inference improves relational . In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, Seattle. ACM
Lafferty JD, McCallum A, Pereira FCN (2001) conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the international conference on machine learning, Washington, DC. Morgan Kaufmann, San Francisco
Lu Q, Getoor L (2003a) Link based . In: Proceedings of the international conference on machine learning, Washington, DC. AAAI
Lu Q, Getoor L (2003b) Link-based using labeled and unlabeled data. In: ICML workshop on the continuum from labeled to unlabeled data in machine learning and data mining, Washington, DC
Macskassy S, Provost F (2007) in networked data: a toolkit and a univariate case study. J Mach Learn Res 8:935–983
Macskassy SA (2007) Improving learning in networked data by combining explicit and mined links. In: Proceedings of the twenty-second AAAI conference on artificial intelligence, Vancouver. AAAI
McDowell LK, Gupta KM, Aha DW (2007) Cautious inference in collective . In: Proceedings of the twenty-second AAAI conference on artificial intelligence, Vancouver. AAAI
Neville J, Jensen D (2007) Relational dependency networks. J Mach Learn Res 8:653–692
Neville J, Jensen D (2000) Iterative in relation data. In: Workshop on statistical relational learning. AAAI
Slattery S, Craven M (1998) Combining statistical and relational methods for learning in hypertext domains. In: International conferences on inductive logic programming, Madison. Springer, London
Taskar B, Abbeel P, Koller D (2002) Discriminative probabilistic models for relational data. In: Proceedings of the annual conference on uncertainty in artificial intelligence, Edmonton. Morgan Kauffman, San Francisco
Taskar B, Guestrin C, Koller D (2003a) Max-margin Markov networks. In: Neural information processing systems. MIT, Cambridge
Taskar B, Wong MF, Abbeel P, Koller D (2003b) Link prediction in relational data. In: Natural information processing systems. MIT, Cambridge
Taskar B, Chatalbashev V, Koller D, Guestrin C (2005) Learning structured prediction models: a large margin approach. In: Proceedings of the international conference on machine learning, Bonn. ACM, New York
Xu L, Wilkinson D, Southey F, Schuurmans D (2006) Discriminative unsupervised learning of structured predictors. In: Proceedings of the international conference on machine learning, Pittsburgh. ACM, New York
Yang Y, Slattery S, Ghani R (2002) A study of approaches to hypertext categorization. J Intell Inf Syst 18(2–3):219–241
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Namata, G., Sen, P., Bilgic, M., Getoor, L. (2014). Collective Classification. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_44-1
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7502-7_44-1
Received:
Accepted:
Published:
Publisher Name: Springer, Boston, MA
Online ISBN: 978-1-4899-7502-7
eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering