Feature Selection Methods for Linked Data: Limitations, Capabilities and Potentials

Marianne Cherrington, David Airehrour, Joan Lu, Qiang Xu, Stephen Wade, Samaneh Madanian

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

Abstract

Feature selection is an important pre-processing, data mining, and knowledge discovery tool for data analysis. By eliminating redundant and irrelevant features from high-dimensional data, feature selection diminishes the 'curse of dimensionality' to improve performance. Data are becoming increasingly complex; heterogeneous data may often be viewed as natural collections of linked objects. Linked data are structured data that are connected with other data sources through the use of semantic queries. It is increasingly prevalent in social media websites and biological networks. Many feature selection methods assume independent and identically distributed data (IID), a condition violated with linked data. In this paper, a review of current feature selection techniques for linked data is presented. Several approaches are examined in various contexts so that performance issues and ongoing challenges can be assessed. The major contribution of this paper is to underscore contemporary uses and limitations of linked data feature selection techniques with the purpose of informing existing capabilities and current potentials for key areas of future research and application.

Original languageEnglish
Title of host publicationBDCAT 2019
Subtitle of host publicationProceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages103-112
Number of pages10
ISBN (Print)9781450370165
DOIs
Publication statusPublished - 2 Dec 2019
Event6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - Auckland, New Zealand
Duration: 2 Dec 20195 Dec 2019
Conference number: 6

Conference

Conference6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
Abbreviated titleBDCAT 2019
CountryNew Zealand
CityAuckland
Period2/12/195/12/19

Fingerprint Dive into the research topics of 'Feature Selection Methods for Linked Data: Limitations, Capabilities and Potentials'. Together they form a unique fingerprint.

  • Cite this

    Cherrington, M., Airehrour, D., Lu, J., Xu, Q., Wade, S., & Madanian, S. (2019). Feature Selection Methods for Linked Data: Limitations, Capabilities and Potentials. In BDCAT 2019: Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (pp. 103-112). New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/3365109.3368792