The problem of determining a proper rejection threshold in classification applications, along with the necessity to define an evaluation criterion for the resulting classifier, are major topics in the process of development of real-world recognition systems. This report reviews the concepts of error, rejection and performance evaluation in the framework of statistical pattern recognition, focusing on the concept of Equal Error Rate (ERR), which is proposed as an objective, threshold-independent measure of the performance of a classifier. A case study is discussed, i.e., evaluation of a speaker identification system based on neural networks, both in terms of error rate and of Equal Error Rate

Rejection and the Equal Error Rate: Principles and a Case Study

Trentin, Edmondo
1995

Abstract

The problem of determining a proper rejection threshold in classification applications, along with the necessity to define an evaluation criterion for the resulting classifier, are major topics in the process of development of real-world recognition systems. This report reviews the concepts of error, rejection and performance evaluation in the framework of statistical pattern recognition, focusing on the concept of Equal Error Rate (ERR), which is proposed as an objective, threshold-independent measure of the performance of a classifier. A case study is discussed, i.e., evaluation of a speaker identification system based on neural networks, both in terms of error rate and of Equal Error Rate
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/1120
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