Transfer Learning for the Choquet Integral

The Choquet integral (ChI) is a proven tool for information aggregation. In prior work, we showed that learning a ChI from data results in missing variables. Herein, we explore two ways to transfer a known ChI from a source domain to a new under sampled target domain. The first method is based on regularization and it listens to the full source domain ChI. The second method optimizes what we can observe (target domain supported variables) and missing variables are the only thing migrated from the source domain. Synthetic experiments, aka we know the truth, are used to show the behavior of these methods with respect to transfering between ChIs.

I. Introduction

A driving force behind computational intelligence (CI) is well developed mathematics. At that, many CI techniques are now learning their parameters from data. However, a caveat of learning models from data is that not all parameters may be effectively learned. Identifying ill-conditioned parameters is not always simple, as many of today’s algorithms are black boxes. However, some CI methods allow for us to know which parameters are supported by data. This begs the question, how do we define unknown, or under supported, parameters.