@inproceedings{ef5a58258621421abc24538f7e4f1dfb,
title = "Feature Selection and Feature Extraction: Highlights",
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.",
keywords = "Benchmark, Comparison, Data Mining, Data Science, Feature Extraction, Feature Selection, Information Science, Survey",
author = "Hiu-Man Wong and Xingjian Chen and Hiu-Hin Tam and Jiecong Lin and Shixiong Zhang and Shankai Yan and Xiangtao Li and Ka-Chun Wong",
year = "2021",
doi = "10.1145/3461598.3461606",
language = "English",
isbn = "978-1-4503-8967-9",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "49--53",
booktitle = "ISMSI 2021",
address = "United States",
note = "5th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2021 ; Conference date: 10-04-2021 Through 11-04-2021",
}