Abstract:

Data stream classification has gradually become a hot topic in the field of data mining in recent years. Most traditional data stream classification algorithms work on data whose values are known and precise, however, they cannot be effectively applied to uncertain data streams which are ubiquitous in practical applications. To establish an appropriate classification model for uncertain data and improve the accuracy of uncertain data stream classification, we propose an ensemble classification algorithm for uncertain data streams, which denotes the uncertain data with an interval and a probability distribution function. We train base classifiers with the C4.5 decision tree classification method and the Naive Bayesian classification method. The proposed algorithm cannot only reasonably process the uncertainty in data streams, but also adapt to the concept drift in an effective way. Experimental results demonstrate the effectiveness and robustness of the proposed algorithm.