A Feature-Enhanced Ranking-Based Classifier for Multimodal Data and Heterogeneous Information Networks

Provided by: University of Illinois at Urbana Champaign
Topic: Big Data
Format: PDF
The authors propose a heterogeneous information network mining algorithm: Feature-enhanced RankClass (F-RankClass). F-RankClass extends RankClass to a unified classification framework that can be applied to binary or multiclass classification of unimodal or multimodal data. They experimented on a multimodal document dataset, 2008/9 Wikipedia selection for schools. For unimodal classification, F-RankClass is compared to Support Vector Machines (SVMs). F-RankClass provides improvements up to 27.3% on the Wikipedia dataset. For multimodal document classification, F-RankClass shows improvements up to 19.7% in accuracy when compared to SVM-based meta-classifiers.

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