International Journal of Modern Engineering Research (IJMER)
Traditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from measurement errors, data staleness, repeated measurements, etc., these kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper, the authors focus on classifying uncertain data by classification and prediction algorithm called setBase. This algorithm introduces new measures for generating, pruning and optimizing.