Theses and Dissertations

Issuing Body

Mississippi State University


Anderson, Derek T.

Committee Member

Younan, Nicholas H.

Committee Member

Abdelwahed, Sherif.

Date of Degree


Document Type

Graduate Thesis - Open Access


Electrical and Computer Engineering

Degree Name

Master of Science


James Worth Bagley College of Engineering


Department of Electrical and Computer Engineering


This thesis aims to simultaneously minimize function error and model complexity for data fusion via the Choquet integral (CI). The CI is a generator function, i.e., it is parametric and yields a wealth of aggregation operators based on the specifics of the underlying fuzzy measure. It is often the case that we desire to learn a fusion from data and the goal is to have the smallest possible sum of squared error between the trained model and a set of labels. However, we also desire to learn as “simple’’ of solutions as possible. Herein, L1-norm regularization of a lexicographically encoded capacity vector relative to the CI is explored. The impact of regularization is explored in terms of what capacities and aggregation operators it induces under different common and extreme scenarios. Synthetic experiments are provided in order to illustrate the propositions and concepts put forth.



l1-norm regularization||capacity learning||minimum model complexity||fuzzy integral||Choquet integral