Multi-criteria based learning of the Choquet integral using Goal programming

Document Type

Conference Proceeding

Publication Date



Department of Electrical and Computer Engineering, Center for Data Sciences


In this paper, we explore a new way to learn an aggregation operator for fusion based on a combination of one or more labeled training data sets and information from one or more experts. One problem with learning an aggregation from training data alone is that it often results in solutions that are overly complex and expensive to implement. It also runs the risk of over-fitting and the quality of that solution is based in large on the size and diversity of the data employed. On the other hand, learning an aggregation based on only expert opinion can be overly subjective and may not result in desired performance for some given task. In order to overcome these shortcomings, we explore a new way to combine both of these important sources. However, conflict between data sets, experts or a combination of the two, can (and often do) occur and must be addressed. Herein, weighted Goal programming, an approach from multi-criteria decision making (MCDM), is employed to learn the fuzzy measure (FM) relative to the Choquet integral (CI) for data/information fusion. This framework provides an interesting way in which we can set the priority order of any number of combination of these two sources. Furthermore, it provides a mechanism to preserve the monotonicity constraints of the FM. We demonstrate results from synthetic experiments across a range of different conflicting and combination of sources scenarios.

Publication Title

2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC)