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A novel extended hierarchical dependence network based on non-hierarchical predictive classes and applications to ageing-related data

Fabris, Fabio and Freitas, Alex A. (2015) A novel extended hierarchical dependence network based on non-hierarchical predictive classes and applications to ageing-related data. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, pp. 711-718. ISBN 978-1-5090-0162-0. E-ISBN 978-1-5090-0163-7. (doi:10.1109/ICTAI.2015.53) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1109/ICTAI.2015.53

Abstract

We propose a novel algorithm for hierarchical classification, the Hierarchical Dependence Network based on non-Hierarchical Predictive Classes (HDN-nHPC) algorithm. HDN-nHPC uses relationships among predictive classes that are not descendants or ancestors of each other to improve classification performance and, at the same time, provide insights to non-obvious predictive class relationships. To test our algorithm and baselines, we have used hierarchical ageing-related datasets where the classes are terms in the Gene Ontology. We have concluded, based on our experiments, that using non-hierarchical predictive class relationships improves the performance of the classification algorithm and that, considering one out of three accuracy measures, the HDN-nHPC is statistically significantly better than the other three algorithms that we have tested, while no statistical significant differences were found on the other two measures.

Item Type: Book section
DOI/Identification number: 10.1109/ICTAI.2015.53
Uncontrolled keywords: data mining, machine learning, hierarchical classification, bioinformatics, probabilistic graphic models
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 28 Apr 2016 17:12 UTC
Last Modified: 25 Sep 2019 11:48 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/55186 (The current URI for this page, for reference purposes)
Fabris, Fabio: https://orcid.org/0000-0001-7159-4668
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