Error rate estimation by mixture decomposition

Abstract We show how error rate estimation may be viewed as a problem of mixture decomposition. We apply the idea to the average conditional error rate approach to estimation and explore the effectiveness of one particular decomposition method by simulation.

[1]  Josef Kittler,et al.  Statistical Properties of Error Estimators in Performance Assessment of Recognition Systems , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  S. Snapinn,et al.  An Evaluation of Smoothed Classification Error- Rate Estimators , 1985 .

[3]  G. McLachlan ASSESSING THE PERFORMANCE OF AN ALLOCATION RULE , 1986 .

[4]  Keinosuke Fukunaga,et al.  The optimal distance measure for nearest neighbor classification , 1981, IEEE Trans. Inf. Theory.

[5]  Larry D. Hostetler,et al.  k-nearest-neighbor Bayes-risk estimation , 1975, IEEE Trans. Inf. Theory.

[6]  P. Hall On the Non‐Parametric Estimation of Mixture Proportions , 1981 .

[7]  D. Hand ESTIMATING CLASS SIZES BY ADJUSTING FALLIBLE CLASSIFIER RESULTS , 1986 .

[8]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[9]  Godfried T. Toussaint,et al.  Bibliography on estimation of misclassification , 1974, IEEE Trans. Inf. Theory.

[10]  D. J. Hand,et al.  Recent advances in error rate estimation , 1986, Pattern Recognit. Lett..

[11]  B. Everitt,et al.  Finite Mixture Distributions , 1981 .

[12]  S. Snapinn,et al.  Classification Error Rate Estimators Evaluated by Unconditional Mean Squared Error , 1984 .

[13]  Geoffrey J. McLachlan,et al.  Error rate estimation on the basis of posterior probabilities , 1980, Pattern Recognit..

[14]  Keinosuke Fukunaga,et al.  A parametrically-defined nearest neighbor distance measure , 1982, Pattern Recognit. Lett..