Dynamic Cost-sensitive Naive Bayes Classification for Uncertain Data

The uncertain data as an important aspect of data mining, has received considerable attention, due to its importance in many applications, but little study has been paid to the cost-sensitive classification on uncertain data, so this paper proposes the dynamic costsensitive Naive Bayes classification for mining uncertain data (DCSUNB). Firstly, we apply the probability density to dispose uncertain discrete and continuous attributes, and give the cost-sensitive Naive Bayes classifier. Secondly, we propose the construction process of dynamic cost, and give the evaluation method for finding the optimal cost and the cost-sensitive classification with sequential test strategy. At last, the dynamic costsensitive Naive Bayes algorithm for uncertain data is structured, which searches the misclassification and test cost spaces to find the optimal cost. By comparing to the other cost-sensitive classification algorithms for uncertain data, the experiments on UCI Datasets show that DCSUNB can improve the classification performance, and reduce effectively the total cost.