How good is good? Bayesian machine-learning estimation of probabilistic benchmarks in noisy datasets and an application to nanofinance+

Abstract

Benchmarks are reference standards that are calculated using key performance indicators (KPIs). Calculating benchmarks in noisy datasets is challenging because noise in KPIs reduces the accuracy and precision of benchmarks. The purpose of this research is to propose a two-step methodology for calculating probabilistic benchmarks in noisy datasets. The research methods are based on swarm optimization and Bayesian machine-learning. Swarm optimization simulates the behavior of birds and applies a novel double-hyperbolic undersampling algorithm to denoise KPIs. Bayesian machine-learning estimates probabilistic benchmarks with denoised KPIs through relevance vector machines. The practical implementation of the methods is illustrated with an application to a database of nanofinance+. The results indicate that the proposed methodology is able to denoise KPIs, estimate probabilistic benchmarks, and properly identify the continuous and discrete factors influencing the accuracy and precision of benchmarks.