A Naive Bayesian Classifier in Categorical Uncertain Data Streams

This paper proposes a novel naive Bayesian classifier in categorical uncertain data streams. Uncertainty in categorical data is usually represented by vector valued discrete pdf, which has to be carefully handled to guarantee the underlying performance in data mining applications. In this paper, we map the probabilistic attribute to deterministic points in the Euclidean space and design a distance based and a density based algorithms to measure the correlations between feature vectors and class labels. We also devise a new pre-binning approach to guarantee bounded computation and memory cost in uncertain data streams classification. Experimental results in real uncertain data streams prove that our density-based naive classifier is efficient, accurate, and robust to data uncertainty.