An approach to the evaluation of the performance of a discrete classifier

Authors:
Vladimir B. Berikov
Affiliations:
Sobolev Institute of Mathematics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia
Venue:
Pattern Recognition Letters
Year:
2002

Citing 5
Cited 3

Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Dimensionality, Sample Size, and Classification Error of Nonparametric Linear Classification Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix

Pattern Recognition Letters
Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)

Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)

The influence of prior knowledge on the expected performance of a classifier

Pattern Recognition Letters
Incorporating confidence in a naive bayesian classifier

UM'05 Proceedings of the 10th international conference on User Modeling
Classifier design given an uncertainty class of feature distributions via regularized maximum likelihood and the incorporation of biological pathway knowledge in steady-state phenotype classification

Pattern Recognition

Quantified Score

Hi-index	0.10

Visualization

Abstract

The problem studied is the behavior of a discrete classifier on a finite learning sample. With naive Bayes approach, the value of misclassification probability is represented as a random function, for which the first two moments are analytically derived. For arbitrary distributions, this allows evaluating learning sample size sufficient for the classification with given admissible misclassification probability and confidence level. The comparison with statistical learning theory shows that the suggested approach frequently recommends significantly smaller learning sample size.