Why collective inference improves relational classification

By David D. Jensen, Jennifer Neville, Brian Gallagher. Knowledge Discovery and Data Mining, 2004

316 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/54c5f8c99175cd22c4dd2c1a7221824daf99fec2

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In Artificial Intelligence

In Statistical and Nonlinear Physics

Citations in the corpus

Graph based anomaly detection and description: a survey (2014, background): “There exist a large amount of research on relational classification methods [Friedman et al., 1999, Jensen et al., 2004, Lu and Getoor, 2003, Macskassy and Provost, 2003, Neville and Jensen, 2000, 2003, Neville et al., 2003, Taskar et al., 2002].”
Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks (2018, background): “Several collective classification algorithms have been proposed to increase accuracy when the network users are interrelated, such as the Iterative Classification Algorithm (ICA) [29] and Gibbs sampling (Gibbs) [30].”
Relational Dependency Networks (2007, background): “, 1998; Neville and Jensen, 2000; Lu and Getoor, 2003), and that the gains over conditional models increase as autocorrelation increases (Jensen et al., 2004).”
Web page classification: Features and algorithms (2009, methodology): “Jensen et al. (2004) investigated the underlying mechanism of collective inference, and argued that the benefit does not only come from a larger feature space, but from modeling dependencies among neighbors and utilizing known class labels.”
A bias/variance decomposition for models using collective inference (2008, methodology): “Keywords Statistical relational learning · Collective inference · Evaluation”
Classification in Networked Data: A Toolkit and a Univariate Case Study (2007, unknown): “Collective inferencing in relational data (Jensen et al., 2004) makes simultaneous statistical judgments regarding the values of an attribute or attributes for multiple linked entities for which some attribute values are not known.”
Leveraging relational autocorrelation with latent group models (2005, background): “Collective models have lower variance than models that represent autocorrelation dependencies indirectly through dependencies on other attributes of related instances [8].”
Collective Classi!cation in Network Data (2008, background): “…has gained attention only in the past five to seven years, initiated by the work of Jennifer Neville and David Jensen (Neville and Jensen 2000; Jensen, Neville, and Gallagher 2004; Neville and Jensen 2007) and the work of Ben Taskar and colleagues (Taskar, Segal , and Koller 2001; Getoor et…”
Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical Study (2008, background): “This work falls into two research areas: (1) collective classification [2,3,7,12,13,14] and (2) graph-based semi-supervised learning (SSL) [15,16].”
Collective Classification in Network Data (2008, background): “…has gained attention only in the past five to seven years, initiated by the work of Jennifer Neville and David Jensen (Neville and Jensen 2000; Jensen, Neville, and Gallagher 2004; Neville and Jensen 2007) and the work of Ben Taskar and colleagues (Taskar, Segal , and Koller 2001; Getoor et…”
Collective Stability in Structured Prediction: Generalization from One Example (2013, result): “In contrast, intuition, empirical results (Jensen et al., 2004; Tsochantaridis et al., 2005) and recent statistical consistency analysis (Xiang & Neville, 2011) suggest that generalization is possible if the single or few examples are large enough, provided they have reasonable internal correlation ”
Semi-supervised multi-task learning of structured prediction models for web information extraction (2011, methodology): “Relational information has been used for collective classification of web documents; see for example, [23, 17, 33].”