A center sliding Bayesian binary classifier adopting orthogonal polynomials

• Proposed a center sliding Bayesian classifier with deterministic solution.
• Proposed to adopt orthogonal polynomials for efficient feature expansion.
• Developed an easy tuning mechanism for the classification framework.
• Provided an extensive evaluation of the proposed classifier.

A center sliding Bayesian design adopting orthogonal polynomials for binary pattern classification is studied in this paper. Essentially, a Bayesian weight solution is coupled with a center sliding scheme in feature space which provides an easy tuning capability for binary classification. The proposed method is compared with several state-of-the-art binary classifiers in terms of their solution forms, decision thresholds and decision boundaries. Based on the center sliding Bayesian framework, a novel orthogonal polynomial classifier is subsequently developed. The orthogonal polynomial classifier is evaluated using two representative orthogonal polynomials for feature mapping. Our experimental results show promising potential of the orthogonal polynomial classifier since it achieves both desired accuracy and computational efficiency.