title: Error-dependency relationships for the naïve Bayes classifier with binary features.
abstract: We derive a tight dependency-related bound on the difference between the Naïve Bayes (NB) error and Bayes error for two binary features and two equiprobable classes. A measure of discrepancy of feature dependencies is proposed for multiple features. Its correlation with NB is shown using 23 real data sets.
authors.affiliation: School of Computer Science, Bangor University, Sean Street, Bangor, Gwynedd, UK. l.i.kuncheva@bangor.ac.uk
authors.forename: Ludmila I
authors.initials: LI
authors.lastname: Kuncheva
authors.forename: Zoë S J
authors.initials: ZS
authors.lastname: Hoare
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016015:Logistic Models; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted