IJRE – Volume 3 Issue 1 Paper 5


Author’s Name : M. Kiruthiga

Volume 03 Issue 01  Year 2016  ISSN No:  2349-252X  Page no: 18-21






Class imbalance is one of the major issues in classification. It degrades the performance of data mining. It mostly occurs by the non-experts labeling the object. Online outsourcing systems, such as Amazon’s Mechanical Turk, allow users to label the same objects with lack of quality. Thus, an agnostic algorithm Positive LAbel frequency Threshold (PLAT) is projected to handle the problem of imbalanced noisy labeling. The main objective is to generate the training dataset and integrate labels of examples. This method is used to resolve the issue of minority sample and also able to deal with imbalanced multiple noisy labeling. The algorithm is applied to the imbalanced dataset collected from UCI repository and the obtained result shows that the PLAT performs better than other methods.


repeated labeling, majority voting, positive and negative labels


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