Derivation of monotone decision models from noisy data

Abstract

Often, in economic decision problems such as credit loan approval or risk analysis, data mining models are required to be monotone with respect to the decision variables involved. If the model is obtained by a blind search through the data, it does mostly not have this property, even if the underlying database is monotone. In this correspondence, we present methods to enforce monotonicity of decision models. We propose measures to express the degree of monotonicity of the data and an algorithm to make data sets monotone. In addition, it is shown that decision trees derived from cleaned data perform better compared to trees derived from raw data