Feature Selection and Feature Extraction : Highlights

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

4 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationISMSI 2021
Subtitle of host publication2021 5th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence
PublisherAssociation for Computing Machinery
Pages49-53
ISBN (Print)978-1-4503-8967-9
Publication statusPublished - 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Title5th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2021
PlaceSeychelles
CityVirtual, Online
Period10 - 11 April 2021

Abstract

In recent years, big data deluges have resulted in exciting data science opportunities. In particular, there is always a desire to extract the most from different data sources. To address it, a promising and recurring task is to perform feature selection and feature extraction. Specifically, the objective is to obtain the non-redundant and informative set of input features (also known as attributes or predictor variables) for downstream data science tasks. In this study, we highlight the existing approaches in both feature selection and feature extraction. In particular, benchmark comparisons are conducted for independent evaluations.

Research Area(s)

  • Benchmark, Comparison, Data Mining, Data Science, Feature Extraction, Feature Selection, Information Science, Survey

Citation Format(s)

Feature Selection and Feature Extraction: Highlights. / Wong, Hiu-Man; Chen, Xingjian; Tam, Hiu-Hin et al.
ISMSI 2021: 2021 5th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence. Association for Computing Machinery, 2021. p. 49-53 (ACM International Conference Proceeding Series).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review