Classification of Uncertain Data Using Selection Algorithm

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, and 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, we focus on classifying uncertain data by classification and prediction algorithm called setBase. This algorithm introduces new measures for generating, pruning and optimizing. Probability distribution function and uncertain data intervals are computed by this kind of algorithm .Based on these new measures, the optimal splitting attribute and splitting value can be identified and used for classification and prediction. Uncertainty in both numerical and categorical data can be processed by this measure. Our experimental results show that setBase has excellent performance even when data is