eprintid: 12481
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/24/81
datestamp: 2023-11-10 03:27:02
lastmod: 2023-11-10 03:27:02
status_changed: 2023-11-10 01:48:51
type: conference_item
metadata_visibility: show
creators_name: Suboh, S.
creators_name: Aziz, I.A.
title: Anomaly Detection with Machine Learning in the Presence of Extreme Value - A Review Paper
ispublished: pub
keywords: Advanced Analytics; Big data; Data Analytics; Data handling; Learning algorithms; Machine learning; Statistics, Data preprocessing; Efficient predictions; Extreme value; Extreme weather events; Real-world; Review papers; Significant harm, Anomaly detection
note: cited By 1; Conference of 2020 IEEE Conference on Big Data and Analytics, ICBDA 2020 ; Conference Date: 17 November 2020 Through 19 November 2020; Conference Code:165892
abstract: Currently, anomaly detection is receiving a great deal of attention due to its importance in many real-world applications especially in data analytics. Anomaly detection has two important purposes in data pre-processing stages: one is to detect and attempts to eliminate them if and only if it is really outlier, while the other requires attention be paid to valid outlier (extreme value) because extreme value themselves carry the significant and critical information, such as extreme weather events. It will lead to significant harm if not detected and treated properly. This paper begins with the summary of anomaly detection, challenges, type of anomalies, extreme value followed by machine learning algorithms approach that deal with anomaly detection. Finally, we provide a conclusion that discusses about the important of detecting clearly between outlier or extreme value in data pre-processing stage, so that an effective and efficient prediction can be achieved later in modelling stage. © 2020 IEEE.
date: 2020
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099698975&doi=10.1109%2fICBDA50157.2020.9289798&partnerID=40&md5=4c4103c83dd316a9d1760d1d424a2ddd
id_number: 10.1109/ICBDA50157.2020.9289798
full_text_status: none
publication: 2020 IEEE Conference on Big Data and Analytics, ICBDA 2020
pagerange: 66-72
refereed: TRUE
isbn: 9781728192468
citation:   Suboh, S. and Aziz, I.A.  (2020) Anomaly Detection with Machine Learning in the Presence of Extreme Value - A Review Paper.  In: UNSPECIFIED.