ANNC: AUC-based feature selection by maximizing nearest neighbor complementarity

Xuemeng Jiang, Jun Wang, Jinmao Wei, Jianhua Ruan, Gang Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Feature selection is crucial for dimension reduction. Dozens of approaches employ the area under ROC curve, i.e., AUC, to evaluate features, and have shown their attractiveness in finding discriminative targets. However, feature complementarity for jointly discriminating classes is generally improperly handled by these approaches. In a recent approach to deal with such issues, feature complementarity was evaluated by computing the difference between the neighbors of each instance in different feature dimensions. This local-learning based approach introduces a distinctive way to determine how a feature is complementarily discriminative given another. Nevertheless, neighbor information is usually sensitive to noises. Furthermore, evaluating merely one-side information of nearest misses will definitely neglect the impacts of nearest hits on feature complementarity. In this paper, we propose to integrate all-side local-learning based complementarity into an AUC-based approach, dubbed ANNC, to evaluate pairwise features by scrutinizing their comprehensive misclassification information in terms of both k-nearest misses and k-nearest hits. This strategy contributes to capture complementary features that collaborate with each other to achieve remarkable recognition performance. Extensive experiments on openly available benchmarks demonstrate the effectiveness of the new approach under various metrics.

Original languageEnglish (US)
Title of host publicationPRICAI 2018
Subtitle of host publicationTrends in Artificial Intelligence - 15th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsByeong-Ho Kang, Xin Geng
PublisherSpringer Verlag
Pages772-785
Number of pages14
ISBN (Print)9783319973036
DOIs
StatePublished - 2018
Externally publishedYes
Event15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018 - Nanjing, China
Duration: Aug 28 2018Aug 31 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11012 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018
CountryChina
CityNanjing
Period8/28/188/31/18

Keywords

  • AUC
  • All-side recognition
  • Feature complementarity
  • Feature selection
  • Nearest neighbors

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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