An Assembly Learning Approaches For Assorted Types of Concept Drift
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 3
Abstract
o Detecting and monitoring changes during the learning process are important areas of research in many industrial applications. The challenging issue is how to diagnose and analyze these changes so that the accuracy of the learning model can be preserved. Recently, ensemble classifiers have achieved good results when dealing with concept drifts. The Information flow mining garners much attention owing to its manifestation in an extensive variety of assertions, such as sensor networks, banking, and telecommunication. One of the most vital tasks in knowledge from information streams is answering to idea implication, unexpected changes of the stream’s core data distribution. Numerous classification procedures that manage with idea implication have been put forward, however, most of them concentrate in one type of change. Focus on the topic of adaptive ensembles that generate component classifiers sequentially from fixed-size blocks of training examples called data chunks. Compared to AUE1, forward a new weighting and updating mechanism as well as modify many other construction details to reduce computational costs and improve classification accuracy. Recently, concept drift has become an important issue while analyzing non-stationary distribution data in data mining. For example, data streams carry a characteristic that data vary by time, and there is probably concept drift in this type of data.
Authors and Affiliations
G. Bakkiyaraj, P. Ayesha Barvin
Keywords
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