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Fidelity Estimation for a Hierarchical Classifier
Author(s) -
Hufnagl P.,
Voss K.
Publication year - 1985
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710270608
Subject(s) - classifier (uml) , fidelity , computer science , artificial intelligence , margin classifier , sample size determination , interval estimation , pattern recognition (psychology) , machine learning , statistics , quadratic classifier , test set , confidence interval , mathematics , telecommunications
To estimate the correct classification rate of a classifier, many different methods exist (test sample, bootstrap, cross validation). The test sample is a method with very small expense. Sometimes, only a small number of objects is available (seldom diseases, high costs for experiments). When we split the sample in training set and test set, we get good or bad fidelity estimations but, unfortunately, vice versa a big or small confidence interval for the estimation. Overcoming this dilemma is only possible for simple classifiers. Such a simple classifier is investigated and a direct fidelity estimation is proposed.