Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

by   Muhammad Aminul Islam, et al.

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.


Fuzzy Integral = Contextual Linear Order Statistic

The fuzzy integral is a powerful parametric nonlin-ear function with uti...

Multi-Level Sensor Fusion with Deep Learning

In the context of deep learning, this article presents an original deep ...

Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond

Explainable Artificial Intelligence (XAI) is an emerging research topic ...

Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications

Image fusion is the process of integrating multiple images of the same s...

Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images

Due to their ability to offer more comprehensive information than data f...

The Utility of Explainable AI in Ad Hoc Human-Machine Teaming

Recent advances in machine learning have led to growing interest in Expl...

Data Fusion: Theory, Methods, and Applications

A proper fusion of complex data is of interest to many researchers in di...

Please sign up or login with your details

Forgot password? Click here to reset