Abstract: Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. This paper presents a novel feature selection algorithm based on Bacteria Foraging Optimization (BFO). The algorithm is applied to coefficients extracted by discrete cosine transforms (DCT). Evolution is driven by a fitness function defined in terms of maximizing the class separation (scatter index). Performance is evaluated using the ORL face database.

Rasleen Jakhar, Navdeep Kaur and Ramandeep Singh, “Face Recognition Using Bacteria Foraging Optimization-Based Selected Features” International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence, 2011. http://dx.doi.org/10.14569/SpecialIssue.2011.010317

@article{Jakhar2011,
title = {Face Recognition Using Bacteria Foraging Optimization-Based Selected Features},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence}
doi = {10.14569/SpecialIssue.2011.010317},
url = {http://dx.doi.org/10.14569/SpecialIssue.2011.010317},
year = {2011},
publisher = {The Science and Information Organization},
volume = {1},
number = {3},
author = {Rasleen Jakhar and Navdeep Kaur and Ramandeep Singh},
}