• Login
    View Item 
    •   Shocker Open Access Repository Home
    • Engineering
    • Electrical Engineering and Computer Science
    • EECS Faculty Scholarship
    • EECS Research Publications
    • View Item
    •   Shocker Open Access Repository Home
    • Engineering
    • Electrical Engineering and Computer Science
    • EECS Faculty Scholarship
    • EECS Research Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Fruit-fly inspired neighborhood encoding for classification

    Date
    2021-08-14
    Author
    Sinha, Kaushik
    Ram, Parikshit
    Metadata
    Show full item record
    Citation
    Sinha, K., & Ram, P. (2021). Fruit-fly inspired neighborhood encoding for classification. Paper presented at the Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1470-1480. doi:10.1145/3447548.3467246
    Abstract
    Inspired by the fruit-fly olfactory circuit, the Fly Bloom Filter [4] is able to efficiently summarize the data with a single pass and has been used for novelty detection. We propose a new classifier that effectively encodes the different local neighborhoods for each class with a per-class Fly Bloom Filter. The inference on test data requires an efficient FlyHash [6] operation followed by a high-dimensional, but very sparse, dot product with the per-class Bloom Filters. On the theoretical side, we establish conditions under which the predictions of our proposed classifier agrees with the predictions of the nearest neighbor classifier. We extensively evaluate our proposed scheme with 71 data sets of varied data dimensionality to demonstrate that the predictive performance of our proposed neuroscience inspired classifier is competitive to the nearest-neighbor classifiers and other single-pass classifiers.
    Description
    Click on the DOI link to access this conference paper at the publishers website (may not be free).
    URI
    https://doi.org/10.1145/3447548.3467246
    https://soar.wichita.edu/handle/10057/22206
    Collections
    • EECS Research Publications

    Browse

    All of Shocker Open Access RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace software copyright © 2002-2023  DuraSpace
    DSpace Express is a service operated by 
    Atmire NV