Global Collective Classification Algorithm

A Global Collective Classification Algorithm is a Collective Classification Algorithm that makes use of a Global Collective Function.

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• Example(s):
  • Conditional Random Fields (Lafferty et al., 2001)
  • Relational Markov Networks (Taskar et al., 2002)
  • Probabilistic Relational Models (Getoor et al., 2002)
  • Markov Logic Networks (Richardon & Domingos, 2006).
• See: Local Collective Classification Algorithm.

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References

2009

• (Bilgic & Getoor, 2009) ⇒ Mustafa Bilgic, and Lise Getoor. (2009). “Reflect and Correct: A misclassification prediction approach to active inference.” In: ACM Transactions on Knowledge Discovery from Data (TKDD), 3(4). doi:10.1145/1631162.1631168
  • There are many collective classification models proposed to date that make different modeling assumptions about these dependencies. They can be grouped into two broad categories. … The second category of collective classification models are global collective classification models. In this case, the collective classification is defined as a global objective function to be optimized. In many cases, a relational graphical model is learned over all the attributes and labels in the graph, and a joint probability distribution over these attributes and labels is learned and optimized. Examples of this category include conditional random fields Lafferty et al. 2001, relational Markov networks Taskar et al. 2002, probabilistic relational models Getoor et al. 2002, and Markov logic networks Richardson and Domingos 2006.

2006

• (Richardon & Domingos, 2006) ⇒ Matthew Richardson, and Pedro Domingos. (2006). “Markov Logic Networks.” In: Machine Learning, 62. doi:10.1007/s10994-006-5833-1.

2002

• (Getoor et al., 2002) ⇒ Lise Getoor, Nir Fridman, Daphne Koller, and Benjamin Taskar. (2002). “Learning Probabilistic Models of Link Structure.” In: Journal Machine Learning Research, 3.
• (Taskar et al., 2002) ⇒ Ben Taskar, Pieter Abbeel, and Daphne Koller. (2002). “Discriminative Probabilistic Models for Relational Data.” In: Proceedings of UAI Conference (UAI 2002).

2001

• (Lafferty et al., 2001) ⇒ John D. Lafferty, Andrew McCallum, and Fernando Pereira. (2001). “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.” In: Proceedings of ICML Conference (ICML 2001).

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