Paper Title
“Error-aware data mining”

Abstract
The data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on the data. Noise can reduce system performance in terms of classification accuracy, time in building a classifier and the size of the classifier. The noise introduced in the attributes available in advance, and a solution to incorporate it into the mining process. More specifically, the noise knowledge to restore original data distributions, which are further used to rectify the model built from noise- corrupted data. This concept by the proposed EA naive Bayes classification algorithm. Experimental comparisons on real-world datasets will demonstrate the effectiveness of this design. In the proposed work adaboost algorithm technique is applied and still more good results are obtained Index Terms- Classification, Data Mining, Naive Bayes (NB), Noise Handling, Noise Knowledge, Adaboost.