TY - JOUR JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) VL - 14322 Y1 - 2024/// UR - 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 A1 - Chaudhari, A. A1 - A.A, H.S. A1 - Raut, R. A1 - Sarlan, A. N1 - cited By 0; Conference of 8th International Visual Informatics Conference, IVIC 2023 ; Conference Date: 15 November 2023 Through 17 November 2023; Conference Code:303189 AV - none SP - 453 PB - Springer Science and Business Media Deutschland GmbH TI - A Novel Approach of Adpative Window 2 Technique and Kalman Filter- â??KalADWIN2â?? for Detection of Concept Drift KW - Learning systems; Machine learning KW - Adaptive windows; ADWIN2; Concept drifts; Drift detectors; KalADWIN2; Learning settings; Machine learning techniques; Non-stationary dynamics; Nonstationary data; Personalized recommendation KW - Kalman filters N2 - 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-â??KalADWIN2â??, as itâ??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. SN - 03029743 EP - 467 ID - scholars20270 ER -