reduction of misclassification cost of imbalanced data using meta-cost algorithm
M.kiruthiga,P.Sangeetha
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 20 December,2016 Pages:1186-1191
Abstract
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. It frequently increases the cost of misclassification which arise due to imbalance.Thus, a meta-cost algorithm is projected to handle the problem of imbalanced noisy labeling and to reduce the misclassification cost. 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 meta-cost algorithm performs better than other methods.
Kewords
repeated labeling, majority voting, positive and negative labels.
Reference
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