Abstract:

Countless challenges in engineering require the intelligent combining (aka fusion) of data or information from multiple sources. The Choquet integral (ChI), a parametric aggregation function, is a well-known tool for multisource fusion, where source refers to sensors, humans and/or algorithms. In particular, a selling point of the ChI is its ability to model and subsequently exploit rich interactions between inputs. For a task with N inputs, the ChI has 2^N interaction variables. Therefore, the ChI becomes intractable quickly in terms of storage and its data-driven learning. Herein, we study the properties of an efficient to store, compute, and ultimately optimize version of the ChI based on a binary fuzzy measure (BFM). The BFM is further motivated by empirical observations in the areas of multi-sensor fusion and hyperspectral image processing. Herein, we provide a deeper understanding of the inner workings, capabilities and underlying philosophy of a BM ChI (BChI). We also prove that two fuzzy integrals, the ChI and the Sugeno integral, are equivalent for a BFM. Furthermore, only a small subset of BFM variables need be stored, which reduces the BChI to a relatively simple look up operation.

Links:

Citation:

D. T. Anderson, M. A. Islam, R. King, N. H. Younan, J. R. Fairley, S. Howington, F. Petry, P. Elmore and A. Zare, "Binary Fuzzy Measures and Choquet Integration for Multi-Source Fusion," Intl. Conf. on Military Technologies 2017. 

@InProceedings{Anderson2017Binary,
Title = {Binary Fuzzy Measures and Choquet Integration for Multi-Source Fusion},
Author = {Derek T. Anderson and Muhammad Aminul Islam and Roger King and Nicolas H. Younan and Joshua R. Fairley and Stacy Howington and Frederick Petry and Paul Elmore and Alina Zare},
Booktitle = {Intl. Conf. on Military Technologies},
Year = {2017}
}