Skip to main content
SpringerLink
Log in
Book cover

Encyclopedia of Machine Learning and Data Mining pp 238–242Cite as

Collective Classification

  • Reference work entry
  • First Online:

Synonyms

Iterative classification; Link-based classification

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...

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   699.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   949.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

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

    Book  Google Scholar 

  • 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

    Google Scholar 

  • Besag J (1986) On the statistical analysis of dirty pictures. J R Stat Soc B-48:259–302

    MathSciNet  MATH  Google Scholar 

  • Carvalho V, Cohen WW (2005) On the collective classification of email speech acts. In: Special interest group on information retrieval, Salvador. ACM

    Book  Google Scholar 

  • Chakrabarti S, Dom B, Indyk P (1998) Enhanced hypertext categorization using hyperlinks. In: International conference on management of data, Seattle. ACM, New York

    Book  Google Scholar 

  • 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

    Book  Google Scholar 

  • Getoor L (2005) Link-based classification. In: Advanced methods for knowledge discovery from complex data. Springer, New York

    Book  MATH  Google Scholar 

  • Getoor L, Taskar B (eds) (2007) Introduction to statistical relational learning. MIT, Cambridge

    MATH  Google Scholar 

  • Getoor L, Segal E, Taskar B, Koller D (2001) Probabilistic models of text and link structure for hypertext classification. In: Proceedings of the IJCAI workshop on text learning: beyond supervision, Seattle

    Google Scholar 

  • Getoor L, Friedman N, Koller D, Taskar B (2002) Learning probabilistic models of link structure. J Mach Learn Res 3:679–707

    MathSciNet  MATH  Google Scholar 

  • Hummel R, Zucker S (1983) On the foundations of relaxation labeling processes. IEEE Trans Pattern Anal Mach Intell 5:267–287

    Article  MATH  Google Scholar 

  • Jensen D, Neville J, Gallagher B (2004) Why collective inference improves relational classification. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, Seattle. ACM

    Google Scholar 

  • 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

    Google Scholar 

  • Lu Q, Getoor L (2003a) Link based classification. In: Proceedings of the international conference on machine learning, Washington, DC. AAAI

    Google Scholar 

  • Lu Q, Getoor L (2003b) Link-based classification using labeled and unlabeled data. In: ICML workshop on the continuum from labeled to unlabeled data in machine learning and data mining, Washington, DC

    Google Scholar 

  • Macskassy S, Provost F (2007) Classification in networked data: a toolkit and a univariate case study. J Mach Learn Res 8:935–983

    Google Scholar 

  • 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

    Google Scholar 

  • McDowell LK, Gupta KM, Aha DW (2007) Cautious inference in collective classification. In: Proceedings of the twenty-second AAAI conference on artificial intelligence, Vancouver. AAAI

    MATH  Google Scholar 

  • Neville J, Jensen D (2007) Relational dependency networks. J Mach Learn Res 8:653–692

    MATH  Google Scholar 

  • Neville J, Jensen D (2000) Iterative classification in relation data. In: Workshop on statistical relational learning. AAAI

    Google Scholar 

  • 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

    Book  Google Scholar 

  • 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

    Google Scholar 

  • Taskar B, Guestrin C, Koller D (2003a) Max-margin Markov networks. In: Neural information processing systems. MIT, Cambridge

    Google Scholar 

  • Taskar B, Wong MF, Abbeel P, Koller D (2003b) Link prediction in relational data. In: Natural information processing systems. MIT, Cambridge

    Google Scholar 

  • 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

    Book  Google Scholar 

  • 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

    Book  Google Scholar 

  • Yang Y, Slattery S, Ghani R (2002) A study of approaches to hypertext categorization. J Intell Inf Syst 18(2–3):219–241

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Maryland, College Park, MD, USA

    Galileo Namata, Prithviraj Sen, Mustafa Bilgic & Lise Getoor

Authors
  1. Galileo Namata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prithviraj Sen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mustafa Bilgic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lise Getoor
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this entry

Cite this entry

Namata, G., Sen, P., Bilgic, M., Getoor, L. (2017). Collective Classification. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_44

Download citation

Publish with us

Policies and ethics

Search

Navigation