eprintid: 20270
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/02/02/70
datestamp: 2024-06-04 14:20:00
lastmod: 2024-06-04 14:20:00
status_changed: 2024-06-04 14:17:06
type: article
metadata_visibility: show
creators_name: Chaudhari, A.
creators_name: A.A, H.S.
creators_name: Raut, R.
creators_name: Sarlan, A.
title: A Novel Approach of Adpative Window 2 Technique and Kalman Filter- �KalADWIN2� for Detection of Concept Drift
ispublished: pub
keywords: Learning systems; Machine learning, Adaptive windows; ADWIN2; Concept drifts; Drift detectors; KalADWIN2; Learning settings; Machine learning techniques; Non-stationary dynamics; Nonstationary data; Personalized recommendation, Kalman filters
note: cited By 0; Conference of 8th International Visual Informatics Conference, IVIC 2023 ; Conference Date: 15 November 2023 Through 17 November 2023; Conference Code:303189
abstract: A recommendation engine (RE) is a machine learning technique that provides personalized recommendations and anticipates a user's future preference for a collection of goods or services. In Online Supervised Learning (OSL) settings like various REs, where data vary over time, Concept Drift (CD) issue usually occurs. There are many CD Detectors in the literature work but the most preferred choice for the non-stationary, dynamic and streaming data is the supervised technique- Adaptive Window (ADWIN) approach. The paper aims towards the limitations of the ADWIN approach, where ADWIN2 approach is more time &memory efficient than ADWIN. The paper also focusses on novel proposed technique of the combination of Kalman Filter and ADWIN2 approach, named-�KalADWIN2�, as it�s the best estimator for detection even in noisy environment. It ultimately helps in fast CD detection in REs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
date: 2024
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175992673&doi=10.1007%2f978-981-99-7339-2_38&partnerID=40&md5=a102fbe24e832ced9f562291f9b42215
id_number: 10.1007/978-981-99-7339-2₃₈
full_text_status: none
publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume: 14322 
pagerange: 453-467
refereed: TRUE
isbn: 9789819973385
issn: 03029743
citation:   Chaudhari, A. and A.A, H.S. and Raut, R. and Sarlan, A.  (2024) A Novel Approach of Adpative Window 2 Technique and Kalman Filter- �KalADWIN2� for Detection of Concept Drift.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14322 .  pp. 453-467.  ISSN 03029743