A modified Naïve Bayes classifier for efficient implementations in embedded systems

In this paper we propose two modifications of the Naïve Bayes (NB) algorithm, in order to reduce its complexity such that it may be effectively implemented with simple operators in embedded computing systems. A first modification is the introduction of a tuning parameter similar to the radius in radial basis function neural networks, it allows improving classification performance. The second modification is the approximation of exponential function with a piecewise-linear function that allows efficient implementation in embedded systems. Using a large set of benchmark problems, comparisons with “standard” NB and with other classifiers (such as SVM and a modified RBF) provided that modified NB learns very fast and may have a very efficient implementation providing a good accuracy.