A feature-enhanced ranking-based classifier for multimodal data and heterogeneous information networks

Scott Deeann Chen, Ying Yu Chen, Jiawei Han, Pierre Moulin

Research output: Contribution to journalConference article

Abstract

We propose a heterogeneous information network mining algorithm: feature-enhanced Rank Class (F-Rank Class). F-Rank Class extends Rank Class to a unified classification framework that can be applied to binary or multiclass classification of unimodal or multimodal data. We experimented on a multimodal document dataset, 2008/9 Wikipedia Selection for Schools. For unimodal classification, F-Rank Class is compared to support vector machines (SVMs). F-Rank Class provides improvements up to 27.3% on the Wikipedia dataset. For multimodal document classification, F-Rank Class shows improvements up to 19.7% in accuracy when compared to SVM-based meta-classifiers. We also study 1) how the structure of the network and 2) how the choice of parameters affect the classification results.

Original languageEnglish (US)
Article number6729588
Pages (from-to)997-1002
Number of pages6
JournalProceedings - IEEE International Conference on Data Mining, ICDM
DOIs
StatePublished - Dec 1 2013
Event13th IEEE International Conference on Data Mining, ICDM 2013 - Dallas, TX, United States
Duration: Dec 7 2013Dec 10 2013

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Keywords

  • classification
  • heterogeneous information network
  • multimodal
  • ranking

ASJC Scopus subject areas

  • Engineering(all)

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