Listwise approaches based on feature ranking discovery

Yongqing Wang, Wenji Mao, Daniel Zeng, Fen Xia

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BLFeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.

Original languageEnglish (US)
Pages (from-to)647-659
Number of pages13
JournalFrontiers of Computer Science in China
Volume6
Issue number6
DOIs
StatePublished - Dec 1 2012

Keywords

  • feature's ranking discovery
  • learning to rank
  • listwise approach

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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