HMC-reliefF: feature ranking for hierarchical multi-label classification

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• dc.contributor.author Slavkov, Ivica
• dc.contributor.author Karcheska, Jana
• dc.contributor.author Kocev, Dragi
• dc.contributor.author Džeroski, Sašo
• dc.date.accessioned 2019-12-03T07:57:45Z
• dc.date.available 2019-12-03T07:57:45Z
• dc.date.issued 2018
• dc.description.abstract In machine learning, the growing complexity of the available data poses an increased challenge for its analysis. The rising complexity is both in terms of the data becoming more high-dimensional as well as the data having a more intricate structure. This emphasizes the need for developing machine learning algorithms that are able to tackle both the high-dimensionality and the complex structure of the data. Our work in this paper focuses on the development and analysis of the HMCReliefF algorithm, which is a feature relevance (ranking) algorithm for the task of Hierarchical Multi-label Classification (HMC). The basis of the algorithm is the RReliefF algorithm for regression that is adapted for hierarchical multi-label target variables. We perform an extensive experimental investigation of the HMC-ReliefF algorithm on several datasets from the domains of image annotation and functional genomics. We analyse the algorithm’s performance in terms of accuracy in a filterlike setting and also in terms of ranking stability for various parameter values. The results show that the HMC-ReliefF can successfully detect relevant features from the data that can be further used for constructing accurate predictive models. Additionally, the stability analysis helps to determine the preferred parameter values for obtaining not just accurate, but also a stable algorithm output.
• dc.description.sponsorship We would like to acknowledge the support of the European Commission through the project MAESTRA – Learning from Massive, Incompletely annotated, and Structured Data (Grant number ICT-2013-612944) and the Human Brain Project (Grant number 604102), and the support of the Spanish Ministry of Economy and Competitiveness, “Centro de Excelencia Severo Ochoa 2013-2017”, SEV-2012-0208.
• dc.format.mimetype application/pdf
• dc.identifier.citation Slavkov I, Karcheska J, Kocev D, Džeroski S. HMC-reliefF: feature ranking for hierarchical multi-label classification. Computer Science and Information Systems. 2018;15(1):187-209. DOI: 10.2298/CSIS170115043S
• dc.identifier.doi http://dx.doi.org/10.2298/CSIS170115043S
• dc.identifier.issn 1820-0214
• dc.identifier.uri http://hdl.handle.net/10230/43057
• dc.language.iso eng
• dc.publisher ComSIS Consortium
• dc.relation.ispartof Computer Science and Information Systems. 2018;15(1):187-209
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/612944
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/604102
• dc.rights Published by ComSIS Consortium under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Feature selection
• dc.subject.keyword Feature ranking
• dc.subject.keyword Structured data
• dc.subject.keyword Hierarchical multilabel classification
• dc.subject.keyword ReliefF
• dc.title HMC-reliefF: feature ranking for hierarchical multi-label classification
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion