Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets
This addresses a practical issue for machine learning practitioners working with distributed or private data sources, but it is an incremental improvement over existing conformal prediction methods.
The paper tackled the problem of generating prediction intervals when training data is split across multiple sources that cannot be pooled, proposing Non-Disclosed Conformal Prediction (NDCP) to combine intervals from independent conformal predictors. The results showed that NDCP produces conservatively valid intervals and improves efficiency compared to using a single source, though not matching the efficiency of pooled data.
Conformal Prediction is a framework that produces prediction intervals based on the output from a machine learning algorithm. In this paper we explore the case when training data is made up of multiple parts available in different sources that cannot be pooled. We here consider the regression case and propose a method where a conformal predictor is trained on each data source independently, and where the prediction intervals are then combined into a single interval. We call the approach Non-Disclosed Conformal Prediction (NDCP), and we evaluate it on a regression dataset from the UCI machine learning repository using support vector regression as the underlying machine learning algorithm, with varying number of data sources and sizes. The results show that the proposed method produces conservatively valid prediction intervals, and while we cannot retain the same efficiency as when all data is used, efficiency is improved through the proposed approach as compared to predicting using a single arbitrarily chosen source.