BIROn - Birkbeck Institutional Research Online

    A new hierarchical redundancy eliminated tree augmented naive Bayes classifier for coping with gene ontology-based features

    Wan, Cen and Freitas, A. (2016) A new hierarchical redundancy eliminated tree augmented naive Bayes classifier for coping with gene ontology-based features. In: The 33rd International Conference on Machine Learning (ICML) Workshop on Computational Biology, 24th June 2016, New York.

    [img]
    Preview
    Text
    1607.01690.pdf

    Download (94kB) | Preview

    Abstract

    The Tree Augmented Naive Bayes classifier is a type of probabilistic graphical model that can represent some feature dependencies. In this work, we propose a Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes (HRE–TAN) algorithm, which considers removing the hierarchical redundancy during the classifier learning process, when coping with data containing hierarchically structured features. The experiments showed that HRE–TAN obtains significantly better predictive performance than the conventional Tree Augmented Naive Bayes classifier, and enhanced the robustness against imbalanced class distributions, in aging-related gene datasets with Gene Ontology terms used as features.

    Metadata

    Item Type: Conference or Workshop Item (Paper)
    Additional Information: Published on arXiv: https://arxiv.org/pdf/1607.01690.pdf
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Bioinformatics, Bloomsbury Centre for (Closed)
    Depositing User: Cen Wan
    Date Deposited: 09 May 2022 17:43
    Last Modified: 09 Aug 2023 12:47
    URI: https://eprints.bbk.ac.uk/id/eprint/30715

    Statistics

    Activity Overview
    6 month trend
    17Downloads
    6 month trend
    98Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item