Bi-capacity Choquet Integral for Sensor Fusion with Label Uncertainty

TL;DR

This paper introduces Bi-MIChI, a novel Choquet integral-based sensor fusion method that handles bipolar data scales and label uncertainty using bi-capacities and Multiple Instance Learning, improving robustness and flexibility.

Contribution

The paper proposes a new fusion framework using bi-capacities and MIL to address bipolar scales and label uncertainty in sensor data, extending existing Choquet integral methods.

Findings

Effective classification and detection on synthetic data

Improved sensor fusion performance on real-world data

Detailed analysis of fuzzy measure behavior

Abstract

Sensor fusion combines data from multiple sensor sources to improve reliability, robustness, and accuracy of data interpretation. The Fuzzy Integral (FI), in particular, the Choquet integral (ChI), is often used as a powerful nonlinear aggregator for fusion across multiple sensors. However, existing supervised ChI learning algorithms typically require precise training labels for each input data point, which can be difficult or impossible to obtain. Additionally, prior work on ChI fusion is often based only on the normalized fuzzy measures, which bounds the fuzzy measure values between [0, 1]. This can be limiting in cases where the underlying scales of input data sources are bipolar (i.e., between [-1, 1]). To address these challenges, this paper proposes a novel Choquet integral-based fusion framework, named Bi-MIChI (pronounced "bi-mi-kee"), which uses bi-capacities to represent the interactions between pairs of subsets of the input sensor sources on a bi-polar scale. This allows for extended non-linear interactions between the sensor sources and can lead to interesting fusion results. Bi-MIChI also addresses label uncertainty through Multiple Instance Learning, where training labels are applied to "bags" (sets) of data instead of per-instance. Our proposed Bi-MIChI framework shows effective classification and detection performance on both synthetic and real-world experiments for sensor fusion with label uncertainty. We also provide detailed analyses on the behavior of the fuzzy measures to demonstrate our fusion process.