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
    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
    Country/TerritoryChina
    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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