Survey on Method of Drift Detection and Classification for time varying data set
K. Wadewale, Sachi V. Desai · 2015
The major problem of online learning or incremental learning is that, target function is frequently changing over time. This problem is commonly known as concept drift. Concept drift can be is further complicated if the dataset is class- imbalanced. There are different learning methods presented so far to handle concept drift like rule-based systems, decision trees, Naive Bayes, support vector machines, instance based learning, ensemble of classifiers, etc. This learning method requires further to combined with methods of drift detection in order to constantly monitor the performance of concept drift, however online changes detection was failed. In literature there are many methods presented for learning from data streams and drift detection, but most of methods failed to achieve speed and accuracy due to data inconsistency. In this project our goal is to present efficient method for online and non-parametric drift detection. This proposed method is based on recently presented Hoeffdings Bounds and HDDM. It handles concept drift regardless of the learning model to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. The existing system however as Naive Bayes classifier are having limitations, there is no scope to improve accuracy of HDDM. The Propose system will be efficiently provide drift detection method for data stream mining to improve accuracy.