Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning

TL;DR

This paper introduces a Bayesian transfer learning-based error estimator that improves classification error assessment in small-sample scenarios by leveraging data from related domains, validated on synthetic and RNA-seq data.

Contribution

It proposes a novel Bayesian MMSE estimator for optimal transfer learning, enhancing error estimation accuracy in small-sample classification tasks.

Findings

Outperforms standard error estimators in small-sample settings

Effective in both synthetic and real-world RNA-seq data

Leverages data from relevant domains for improved accuracy

Abstract

Classification has been a major task for building intelligent systems as it enables decision-making under uncertainty. Classifier design aims at building models from training data for representing feature-label distributions--either explicitly or implicitly. In many scientific or clinical settings, training data are typically limited, which makes designing accurate classifiers and evaluating their classification error extremely challenging. While transfer learning (TL) can alleviate this issue by incorporating data from relevant source domains to improve learning in a different target domain, it has received little attention for performance assessment, notably in error estimation. In this paper, we fill this gap by investigating knowledge transferability in the context of classification error estimation within a Bayesian paradigm. We introduce a novel class of Bayesian minimum mean-square error (MMSE) estimators for optimal Bayesian transfer learning (OBTL), which enables rigorous evaluation of classification error under uncertainty in a small-sample setting. Using Monte Carlo importance sampling, we employ the proposed estimator to evaluate the classification accuracy of a broad family of classifiers that span diverse learning capabilities. Experimental results based on both synthetic data as well as real-world RNA sequencing (RNA-seq) data show that our proposed OBTL error estimation scheme clearly outperforms standard error estimators, especially in a small-sample setting, by tapping into the data from other relevant domains.