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Dependency network methods for hierarchical multi-label classification of gene functions

Fabris, Fabio and Freitas, Alex A. (2014) Dependency network methods for hierarchical multi-label classification of gene functions. In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, pp. 241-248. E-ISBN 978-1-4799-4518-4. (doi:10.1109/CIDM.2014.7008674) (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)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/CIDM.2014.7008674

Abstract

Hierarchical Multi-label Classification (HMC) is a challenging real-world problem that naturally emerges in several areas. This work proposes two new algorithms using a Probabilistic Graphical Model based on Dependency Networks (DN) to solve the HMC problem of classifying gene functions into pre-established class hierarchies. DNs are especially attractive for their capability of using traditional, “out-of-the-shelf”, classification algorithms to model the relationship among classes and for their ability to cope with cyclic dependencies, resulting in greater flexibility with respect to Bayesian Networks. We tested our two algorithms: the first is a stand-alone Hierarchical Dependency Network (HDN) algorithm, and the second is a hybrid between the HDN and the Predictive Clustering Tree (PCT) algorithm, a well-known classifier for HMC. Based on our experiments, the hybrid classifier, using SVMs as base classifiers, obtained higher predictive accuracy than both the standard PCT algorithm and the HDN algorithm, considering 22 bioinformatics datasets and two out of three predictive accuracy measures specific for hierarchical classification (AU(PRC) and AUPRC w).

Item Type: Book section
DOI/Identification number: 10.1109/CIDM.2014.7008674
Uncontrolled keywords: dependency network, hierarchical classification, data mining, machine learning, gene function, bioinformatics
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 30 Jan 2015 15:45 UTC
Last Modified: 25 Sep 2019 11:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/47018 (The current URI for this page, for reference purposes)
Fabris, Fabio: https://orcid.org/0000-0001-7159-4668
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