Prediction of Regional Voting Outcomes Using Heterogeneous Collective Regression

Abstract

Increasingly, many important domains in the world can be viewed as networks of linked nodes: people connected by social network friendships, "webpages connected by hyperlinks, and even geo-political areas connected by proximity and common interests. To leverage these links for prediction and analysis tasks, Machine Learning researchers have developed multiple techniques for link-based classification (LBC). While LBC can substantially improve prediction accuracy in some domains, current limitations greatly restrict its applicability when used to evaluate heterogeneous domains (e.g., when the collection of nodes" under study are actually drawn from multiple populations). Additionally, traditional LBC predicts only categorical outputs, while link-based regression and the prediction of continuous outputs have been left largely unexplored. One such application that requires continuous outputs involves elections.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 21, 2018
Accession Number
AD1054416

Entities

People

  • David J. Liedtka

Organizations

  • United States Naval Academy

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Bayesian Inference
  • Bayesian Networks
  • Classification
  • Computer Science
  • Data Science
  • Information Science
  • Learning
  • Machine Learning
  • Network Science
  • Normal Distribution
  • Predictive Modeling
  • Probability
  • Supervised Machine Learning
  • United States
  • United States Naval Academy

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Computer Networking
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks