Multiple Costs and Their Combination in Cost Sensitive Learning
Zhenxing Qin · UTS ePRESS (University of Technology Sydney) · 2007
Cost sensitive learning is firstly defined as a procedure of minimizing the costs of classification errors.It has attracted much attention in the last few years.Being cost sensitive has the strength to handle the unbalance on the misclassification errors in some real world applications.Recently, researchers have considered how to deal with two or more costs in a model, such as involving both of the misclassification costs (the cost for misclassification errors) and attribute test costs (the cost incurs as obtaining the attribute's value) [Tur95, GGR02, LYWZ04], Cost sensitive learning involving both attribute test costs and misclassification costs is called test cost sensitive learning that is more close to real industry focus, such as medical research and business decision.Current test cost sensitive learning aims to find an optimal diagnostic policy (simply, a policy) with minimal expected sum of the misclassification cost and test cost that specifies, for example which attribute test is performed in next step based on the outcomes of previous attribute tests, and when the algorithm stops (by choosing to classify).A diagnostic policy takes the form of a decision tree whose nodes specify tests and whose leaves specify classification actions.A challenging issue is the choice of a reasonable one from all possible policies.This dissertation argues for considering both of the test cost and misclassification cost, or even more costs together, but doubts if the current way, summing up the two costs, is the only right way.Detailed studies are needed to ensure the ways of combination make sense and be "correct", dimensionally as well as semantically.This IV First, I would like to take this opportunity to express my sincere gratitude to my supervisor, Professor Chengqi Zhang, for his unreserved