oncept Drift for obtaining Accurate Insight on ProcessExecution
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 3
Abstract
Abstract: Most business processes update with respect time, existing or old process mining strategies used toresolve those processes when they are under the stable position. My attempt is to improve the existing DriftDetection in complex Datasets. Organizations would be interested in discovering the progression of change.There are other applications such as deriving a configurable model for the process change. Online learningalgorithms manage concept drifts in process. There are many dissimilar diversity levels in learning machinesare needed for keep high generalization. Motivated by this concept, propose a new online learning model namedas Diversity for Dealing with Drifts (DDD). By Comparing DDD with other method EDDM conclude that DDDgives higher accuracy. It keeps ensembles with many dissimilar diversity levels and can achieve good accuracythan EDDM. Moreover, it is strong and precise when there are false positive drift detections occur than EDDM.DDD always performed drift handling under many different situations, with very low errors.
Authors and Affiliations
Vidya N. Chavada , Pratik Kumar
Keywords
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