Enhancing Concept Drift Classification in Computer Networks with Artificial Intelligence through NCDC-DM: A Novel Approach Utilizing Diversity Measure - Chetan Nimba Aher., Veena Suhas Bhende, Vaibhav B. Magdum, Rajkumar K. Chougale, Manoj Tarambale, Amrapali Shivajirao Chavan (International Journal of Intelligent Systems and Applications in Engineering, 2024)
nowadays data stream mining has been essential area for research work which has been getting waste focus because it was utilized along a huge counts of applications, like telecommunication, networks of sensors & banking. Important issue was effecting mining of data stream was concept drift. When contact between target variable & input data modifications at this time. In last decade there are many classification techniques of concept drift was proposed, that either getting problem of high cost along conditions regards memory either run time either, that was not quack along conditions of classification speed. This paper proposes a technique which known as Novel Concept Drift Classification utilizing Diversity Measure (NCDC-DM), along reduction of less memory & less time it is reacting quickly. Under proposed system there is collaboration between disagreement measure & diversity measure, which known through static learning along scenarios of streaming utilizing test of page & utilizes these calculations comparatively along classification technique of ten drift utilizing various scenarios of drift. Outputs of research shows that proposed technique most efficient & it has capability of faster classification concept drift & it’s compared with existing ADASYN, EACD, HLFR methods.