Naive Bayes Classification of Uncertain Data

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. With uncertainty, the value of each data item is represented by a probability distribution function (pdf). In this paper, we propose a novel naive Bayes classification algorithm for uncertain data with a pdf. Our key solution is to extend the class conditional probability estimation in the Bayes model to handle pdf's. Extensive experiments on UCI datasets show that the accuracy of naive Bayes model can be improved by taking into account the uncertainty information. Keywords-Uncertain data mining; naive Bayes model

• Type: Journal
• Year: 2009
• Where: ICDM
• Authors: Jiangtao Ren, Sau Dan Lee, Xianlu Chen, Ben Kao, Reynold Cheng, David Wai-Lok Cheung
• Keywords: Class Conditional Probability, Data Mining, ICDM 2009, Naive Bayes, Naive Bayes Model