Learning Naive Bayes Classifier from Noisy Data
Yirong Yang, Yi Xia, Yün Chi, Richard R. Muntz · 2003
Classification is one of the major tasks in knowledge discovery and data mining. Naive Bayes classifier, in spite of its simplicity, has proven surprisingly effective in many practical applications. In real datasets, noise is inevitable, because of the imprecision of measurement or privacy preserving mechanisms. In this paper, we develop a new approach, LinEar-Equation-based noise-aWare bAYes classifier (LEEWAY), for learning the underlying naive Bayes classifier from noisy observations. Using