Optimizing support vector machine analysis in low density biological data sets

Pablo Rivas, Sharon Moore, Urszula Iwaniec, Russell Turner, Kathy Grant, Erich Baker

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

    Abstract

    We explore the effectiveness of Support Vector Machines (SVM) for classification in a sparse data set. Non-human primate models are utilized to analyze Alcohol Use Disorders (AUDs); however, the resulting data have a limited sample size. The challenge of low sample numbers and low replicates are explored using a variety of optimization strategies for feature extraction, including correlation, entropy, density, linear support vector machines for regression (SVR), backward SVR, and forward SVR. We investigate these approaches against the backdrop of the relationship between alcohol consumption and tibial bone mineral density. The results indicate that machine learning (ML) can effectively be used in cases of low and diverse biological data sets. The best relevance feature ranking strategies are correlation, SVR forward, and SVR backward.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1357-1361
    Number of pages5
    ISBN (Electronic)9781728113609
    DOIs
    StatePublished - Dec 2018
    Event2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018 - Las Vegas, United States
    Duration: Dec 13 2018Dec 15 2018

    Publication series

    NameProceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018

    Conference

    Conference2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
    CountryUnited States
    CityLas Vegas
    Period12/13/1812/15/18

    Keywords

    • Bone modeling
    • Machine learning
    • Relevance feature mapping
    • Support vector regression

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Computer Science Applications
    • Hardware and Architecture
    • Information Systems and Management
    • Control and Optimization
    • Modeling and Simulation
    • Artificial Intelligence

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