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--- public:cost-sensitive_classification [2009/06/18 07:51]
htlin 建立
+++ public:cost-sensitive_classification [2019/06/25 13:25]
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-====== Introduction ======
-Classification is an important problem in machine learning. It can be used
-in a variety of applications, such as separating apples, oranges, and bananas
-automatically. Traditionally, the regular classification setup aims at
-minimizing the rate of future mis-prediction errors. Nevertheless, in some
-applications, it is needed to treat different types of mis-prediction errors
-differently. For instance, in a medical decision system, the cost of
-mis-predicting a cancerous patient as a healthy one may be higher than
-the other way around. In an animal recognition system, the silliness of
-mis-predicting a person as a fish may be higher than the silliness of
-mis-predicting her/him as a monkey. Such a need can be formalized as the
-cost-sensitive classification setup, which is drawing much research attention
-because of its many potential applications, including targeted marketing,
-fraud detection, and web analysis.

Computational Learning Lab.