# Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications

arXiv: https://arxiv.org/abs/1803.04048 · HTML: https://mlsanity.com/paper/1803.04048/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Efficient Multi-Resolution Fusion for Remote Sensing Data with Label Uncertainty](https://mlsanity.com/paper/2402.05045/) (2024)
- [Bi-capacity Choquet Integral for Sensor Fusion with Label Uncertainty](https://mlsanity.com/paper/2409.03212/) (2024)
- [Parameter-efficient Dual-encoder Architecture with Differentiable Choquet Integral Fusion for Underwater Acoustic Classification](https://mlsanity.com/paper/2606.02341/) (2026)

## Abstract (arXiv)

In classifier (or regression) fusion the aim is to combine the outputs of several algorithms to boost overall performance. Standard supervised fusion algorithms often require accurate and precise training labels. However, accurate labels may be difficult to obtain in many remote sensing applications. This paper proposes novel classification and regression fusion models that can be trained given ambiguosly and imprecisely labeled training data in which training labels are associated with sets of data points (i.e., "bags") instead of individual data points (i.e., "instances") following a multiple instance learning framework. Experiments were conducted based on the proposed algorithms on both synthetic data and applications such as target detection and crop yield prediction given remote sensing data. The proposed algorithms show effective classification and regression performance.