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:
- Recognition rate: The higher, the better. Some people prefer to use the error rate, which is equal to 1 minus the recognition rate.
- Computation load: The lower, the better. In fact, we have two types of computation loads:
- Computation load at the design stage
- Computation load at the application stage
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 (資料分群與樣式辨認)