Call For Paper Volume:4 Issue:8 Aug'2017 |

Mining Data Streams Using Accuracy Updated Ensemble Classifiers

Publication Date : 28/02/2015



Author(s) :

JONES MERLIN.E , JAYANTHI.S , RAMYA DEVI.R , SUDHA.C , SUDHA.C.


Volume/Issue :
Volume 2
,
Issue 2
(02 - 2015)



Abstract :

Information stream mining garnered much attention owing to its manifestation in a 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.  


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Mining Data Streams Using Accuracy Updated Ensemble Classifiers

February 27, 2015