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

Enhanced Non-Parametric Summarization Using Concept Evolution

Publication Date : 02/04/2015

Author(s) :

Balaji.R , Jayanthi.S , Dinesh Raj.R , Dhivya.E.

Volume/Issue :
Volume 2
Issue 3
(04 - 2015)

Abstract :

To abstract a concept from the raw data user may choose any classification algorithm of processor interest, or choose one that appears to be good at learning the current data. Information is commonly used and can achieve reasonable classification accuracy in general. A trigger detection algorithm finds instances, across which the underlying concept has changed and the prediction model should be modified. It is especially important when concept shifts. A classification methodology is used here with two parameters window size and error threshold. The beginning of the window is always a misclassified instance. When the window is full and its error rate exceeds the error threshold, the beginning instance is taken as a trigger; otherwise, the beginning of the window is slid to the next misclassified instance (if there is any) and previous instances are dropped from the window.  The temporarily package holds potential novel class instances. These instances are analyzed periodically in order to detect novel class, which is explained in the next paragraph, needs to be cleared periodically to remove instances that no longer contribute to novel class detection. Besides, instances in that have reached classification deadline

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Enhanced Non-Parametric Summarization Using Concept Evolution

March 31, 2015