A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques - M. S., Abdul Razak; Nirmala, C. R.; Aljohani, Maha; Sreenivasa, B. R. (Frontiers in Artificial Intelligence, 2022)
Financial market is the platform to produce data stream continuously and around 1.145 Trillion MB of data per day. Estimation and the analysis of unknown or dynamic behaviours of these systems is one of the challenging tasks. Analysis of these systems is very much essential to strengthen the environmental parameters to stabilize society activities. This can elevate the living style of the society to the next level. In this connection, the proposed paper is trying to accommodate the financial data stream using sliding window approach and random forest algorithm to provide a solution to handle concept drift in financial market to stabilize the behaviour of the system through the drift estimation. The proposed approach provides promising results in terms of accuracy in detecting concept drift over the state of existing drift detection methods like OCDD, ADWIN, and Page-Hinckley test.