MassBayes: a new generative classifier with multi-dimensional likelihood estimation

Sunil Aryal, Kai Ming Ting

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

    3 Citations (Scopus)

    Abstract

    Existing generative classifiers (e.g., BayesNet and AnDE) make independence assumptions and estimate one-dimensional likelihood. This paper presents a new generative classifier called MassBayes that estimates multi-dimensional likelihood without making any explicit assumptions. It aggregates the multi-dimensional likelihoods estimated from random subsets of the training data using varying size random feature subsets. Our empirical evaluations show that MassBayes yields better classification accuracy than the existing generative classifiers in large data sets. As it works with fixed-size subsets of training data, it has constant training time complexity and constant space complexity, and it can easily scale up to very large data sets.
    Original languageEnglish
    Title of host publicationAdvances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013 Proceedings, Part I
    EditorsJian Pei, Vincent S Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu
    Place of PublicationGermany
    PublisherSpringer-Verlag London Ltd.
    Pages136 - 148
    Number of pages13
    ISBN (Print)9783642374524
    DOIs
    Publication statusPublished - 2013
    EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2013 - Gold Coast, Australia
    Duration: 14 Apr 201317 Apr 2013
    Conference number: 17th
    https://link.springer.com/book/10.1007/978-3-642-37453-1

    Conference

    ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2013
    Abbreviated titlePAKDD 2013
    CountryAustralia
    CityGold Coast
    Period14/04/1317/04/13
    Internet address

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