Fast Feature Selection for Naive Bayes Classification in Data Stream Mining
Patricia E.N. Lutu · 2013
Stream mining is the process of mining a continuous, ordered sequence of data items in real-time. Naive Bayes (NB) classification is one of the popular classification methods for stream mining because it is an incremental classification method whose model can be easily updated as new data arrives. It has been observed in the literature that the performance of the NB classifier improves when irrelevant features are eliminated from the modeling process. This paper reports studies that were conducted to identify efficient computational methods for selecting relevant features for NB classification based on the sliding window method of stream mining. The paper also provides experimental results which demonstrate that continuous feature selection for NB stream mining provides high levels of predictive performance.