The Mixed-Integer Linear Programming for Cost-Constrained Decision Trees with Multiple Condition Attributes

Abstract

In many real-world applications, cost factors play a significant role. Costs have been taken into consideration in numerous previous studies in machine learning, especially, in building decision trees. This research also considers a cost-sensitive decision tree construction problem with an assumption that test costs must be paid to obtain the values of the decision attribute and a record must be classified without exceeding the spending cost threshold. Moreover, our problem considers records with multiple condition attributes. We construct a cost-constrained decision tree using a Mixed-Integer formulation, which enables us to identify the optimal trees. The experimental results demonstrate that our formulation satisfactorily handles small data sets with multiple condition attributes under different cost constraints.