Title :
A modified Naïve Bayes classifier for efficient implementations in embedded systems
Author_Institution :
Dept. of Appl. Electron. & Inf. Eng., Univ. “Politeh.” of Bucharest, Bucharest, Romania
fDate :
June 30 2011-July 1 2011
Abstract :
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.
Keywords :
Bayes methods; belief networks; benchmark testing; embedded systems; pattern classification; piecewise linear techniques; radial basis function networks; NB algorithm; Naïve Bayes algorithm; benchmark problems; classification performance; embedded computing systems; embedded systems; exponential function; modified naïve Bayes classifier; piecewise-linear function; radial basis function neural networks; tuning parameter; Complexity theory; Computational modeling; Niobium; Support vector machine classification; Training; Tuning;
Conference_Titel :
Signals, Circuits and Systems (ISSCS), 2011 10th International Symposium on
Conference_Location :
lasi
Print_ISBN :
978-1-61284-944-7
DOI :
10.1109/ISSCS.2011.5978765