Fast Incremental Naïve Bayes with Kalman Filtering

In recent years an increasing number of applications, IoT sensors and websites have produced endless streams of data. These data streams are not only unbounded, but their characteristics dynamically change over time, generating a phenomenon called concept drift. The standard machine learning models do not work properly in this context and new techniques have been developed in order to tackle these challenges. In this paper we present a new Naïve Bayes algorithm that exploits Kalman Filter, namely KalmanNB, to manage automatically concept drift. Furthermore, we want to investigate when this new approach, which directly follows the values of data's attributes, is better than the standard strategy, which monitors the performance of the model in order to detect a drift. Extensive experiments on both artificial and real datasets with concept drifts reveal that KalmanNB is a valid alternative to the state-of-the-art algorithms, outperforming the latter especially in case of recurring concept drifts.