TY  - CONF
A1  - Hussain, S.S.
A1  - Hashmani, M.
A1  - Uddin, V.
A1  - Ansari, T.
A1  - Jameel, M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112411453&doi=10.1109%2fICCOINS49721.2021.9497232&partnerID=40&md5=f86d6297cb2d41fdddc5b26002f311b9
EP  - 141
Y1  - 2021///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781728171517
N2  - Data concept drift is reported as one of the critical performance degradation phenomena in Machine Learning, especially for volumetric data. Besides, the concept drift annotation is also one of the major research problems in the said domain. In this paper, a novel approach for data concept drift detection is presented. Moreover, the performance after removing the instances with concept drift is also compared with the original dataset on various machine learning algorithms. Specifically, the concept using Euclidean distance in clusters and the mutual information of an instance refer to the degree of concept drift of the instance. The said approach has been employed on the SEA dataset. © 2021 IEEE.
N1  - cited By 3; Conference of 6th International Conference on Computer and Information Sciences, ICCOINS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:170762
TI  - A Novel Approach to Detect Concept Drift Using Machine Learning
ID  - scholars14711
SP  - 136
KW  - Machine learning; Volumetric analysis
KW  -  Concept drifts; Euclidean distance; Mutual informations; Performance degradation; Research problems; Volumetric data
KW  -  Learning algorithms
AV  - none
ER  -