6-1 Intro. to Recognition Rate Estimate of Classifiers (������)



Once we have constructed a classifier using a certain pattern recognition method, we need to evaluate its performance objectively. The performance evaluation of a classifier usually involves two factors:

The computation load of a classifier depends on the underlying classifier a lot, which we shall not go into detail in this chapter. Instead, the focus of this chapter is to cover several methods for estimating the ideally true recognition rate for a given classifier and a dataset.

Moreover, for a simple binary classification problem, the misclassified cases can be divided into two types of false positive and false negative. We shall also address the issue of selecting a threshold for the classifier based on the cost of false positive and false negative.

Data Clustering and Pattern Recognition (ƤsP˦{)