A DECISION TREE LEARNER FOR COST-SENSITIVE BINARY CLASSIFICATION
DANIEL DOS SANTOS MARQUES · 2016
Classification problems have been widely studied in the machine learning literature, generating applications in several areas.However, in a number of scenarios, misclassification costs can vary substantially, which motivates the study of Cost-Sensitive Learning techniques.In the present work, we discuss the use of decision trees on the more general Example-Dependent Cost-Sensitive Problem (EDCSP), where misclassification costs vary with each example.One of the main advantages of decision trees is that they are easy to interpret, which is a highly desirable property in a number of applications.We propose a new attribute selection method for constructing decision trees for the EDCSP and discuss how it can be efficiently implemented.Finally, we compare our new method with two other decision tree algorithms recently proposed in the literature, in 3 publicly available datasets.