KNN+X

Daniel Gilkarov, Lee Ad Gottlieb, Hillel Ohayon

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    We introduce a new paradigm for classifying a query point based on its nearest k neighbors: Having computed the k nearest neighbors of a query, we use a learning algorithm to determine the label to be assigned to the query. This paradigm is a generalization of the well-known weighted k nearest neighbor class of algorithms, and other individual instances of it have been studied as well, for example, where the classifier used is Support Vector Machines or a neural net. Within this paradigm, we study and test new learning classifiers, and find that combining KNN with each classifier typically yields higher accuracy than using each method alone. This suggests using KNN as a pre-processing step for a wide range of familiar machine-learning algorithms.

    Original languageEnglish
    Title of host publicationCyber Security, Cryptology, and Machine Learning - 8th International Symposium, CSCML 2024, Proceedings
    EditorsShlomi Dolev, Michael Elhadad, Mirosław Kutyłowski, Giuseppe Persiano
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages299-309
    Number of pages11
    ISBN (Print)9783031769337
    DOIs
    StatePublished - 2025
    Event8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024 - Be'er Sheva, Israel
    Duration: 19 Dec 202420 Dec 2024

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume15349 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024
    Country/TerritoryIsrael
    CityBe'er Sheva
    Period19/12/2420/12/24

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