[chinese][english] (½Ðª`·N¡G¤¤¤åª©¥»¨Ã¥¼ÀH^¤åª©¥»¦P¨B§ó·s¡I)
Slides
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:
©Ò¿×¤ÀÃþ¾¹ªº¡u®Ä¯àµû¦ô¡v¡]performance evaluation¡^¡A¬O«ü§Ú̦b³]p¤@Ó¤ÀÃþ¾¹¤§«á¡A¦p¦ó¥H¤@Ó¦³®Äªº¤è¦¡¨Ó¹w¦ô¦¹¤ÀÃþ¾¹ªº¯à¤O¡A³q±`¥i¥H¤À¬°¨â³¡¤À¨Óµû¦ô¡G
- 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
- ¹Bºâ¶q¡G¶V¤p¶V¦n¡A¦¹³¡¤À¤S¥]§t
- ³]p®Éªº¹Bºâ¶q
- ¿ëÃѮɪº¹Bºâ¶q
- ¿ëÃѲv¡G¶V°ª¶V¦n¡C¡u¿ëÃѲv¡v¡]recognition rate¡^¬O«üªº¬Oµo¥Í¤ÀÃþ¿ù»~ªº¾÷²v¡A»P¿ëÃѲv¬Û¹ïªº¥t¤@Ó¦Wµü¬O¡u¿ù»~²v¡v¡]error rate¡^¡A«üªº¬O¥¿½T¤ÀÃþªº¾÷²v¡A¨âªÌÁ`©MÀ³¸Óµ¥©ó100%¡C
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.
¤£¦Pªº¤ÀÃþ¾¹¡A·|¦³¤£¦Pªº¹Bºâ¶q¡A¥»³¹±N«ÂI©ñ¦b¿ëÃѲvªº¦ô´ú¡A¦Ó¤£°Q½×¹Bºâ¶q¡C
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.
¥Ñ©ó¦b²{¹ê¥@¬É¤¤¡A©Ò¦³ªº¼Ë¥»¸ê®Æ¡]sample data¡^³£¬O¦³ªº¡A¸ê®Æªº¦¬¶°¹Lµ{¥»¨´Nn¯Ó¶O®É¶¡»P¤H¤O¡A¦]¦¹¼Ë¥»¸ê®Æ¤]´N¯q§Î¬Ã¶Q¡C¼Ë¥»¸ê®Æ¶V¦h¡A§Ú̳]p¥X¨Óªº¤ÀÃþ¾¹¤]·|¶Vºë·Ç¡A¦ý¬O¬°¤F´ú¸Õ©Ò³]p¥X¨Óªº¤ÀÃþ¾¹ªº®Ä¯à¡A©Ò¥H¦b¶i¦æ¼Ë¦¡¿ëÃѨt²Îªº³]p¬yµ{¤¤¡A§ÚÌ·|±N©Ò¦³ªº¼Ë¥»¸ê®Æ¤Á¦¨¨â³¡¤À¡G
¤£¦Pªº¸ê®Æ¤Á¤À¤è¦¡¡A´N¹ïÀ³¨ì¤£¦Pªº¿ù»~²v¦ô´ú¤è¦¡¡A½Ð¨£¦U¤p¸`¸Ôz¡C
- °V½m¸ê®Æ¡]training data¡^¡G¤SºÙ¬°¡u³]p¸ê®Æ¡v¡]design data¡^¡A§Ú̥Φ¹¸ê®Æ¨Ó³]p¤ÀÃþ¾¹¡C
- ´ú¸Õ¸ê®Æ¡]test data¡^¡G§Ú̥Φ¹¸ê®Æ¨Ó´ú¸Õ¤ÀÃþ¾¹ªº®Ä¯à¡C
Data Clustering and Pattern Recognition (¸ê®Æ¤À¸s»P¼Ë¦¡¿ë»{)