Adapting aerial root classifier missing data processor in data stream decision tree classification

Abstract

This work has contributed to the development of a classification method that can deal with the missing data problems. This method called ARC-CVFDT was developed in order to adapt the Aerial Root Classifier missing data processor to the Data Stream decision tree classification method. it offers a higher level of accuracy and adaptation with most of DSM challenges such as Concept Drifting. Keywords: Missing data, Classification, Decision Tree, Data Stream, Machine Learning.