Abstract
We analyze a Relational Neighbor (RN) classifier, a simple relational\npredictive model that predicts only based on class labels of related neighbors,\nusing no learning and no inherent attributes.We show that it performs surprisingly\nwell by comparing it to more complex models such as Probabilistic Relational\nModels and Relational Probability Trees on three data sets from published work.\nWe argue that a simple model such as this should be used as a baseline to assess\nthe performance of relational learners.
Keywords
Simple (philosophy)Classifier (UML)Computer scienceArtificial intelligencePattern recognition (psychology)EpistemologyPhilosophy
Affiliated Institutions
Related Publications
Classification in Networked Data: A Toolkit and a Univariate Case Study
This paper is about classifying entities that are interlinked with entities for which the class is known. After surveying prior work, we present NetKit, a modular toolkit for cl...
A Practical Transfer Learning Algorithm for Face Verification
Face verification involves determining whether a pair of facial images belongs to the same or different subjects. This problem can prove to be quite challenging in many importan...
Publication Info
- Year
- 2003
- Type
- report
- Citations
- 244
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
244
OpenAlex
Cite This
Sofus A. Macskassy,
Foster Provost
(2003).
A Simple Relational Classifier.
.
https://doi.org/10.21236/ada452802
Identifiers
- DOI
- 10.21236/ada452802