Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains
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Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others’ shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or “architectures” as we call them when they are structurally different, so it is now challenging to prescribe which one is better for a specific collection of sensors and a particular application environment, other than by trial and error. We propose an approach capable of predicting the best fusion architecture (from predefined options) for a given dataset. This method involves the construction of a meta-dataset where statistical characteristics from the original dataset are extracted. One challenge is that each dataset has a different number of variables (columns). Previous work took the principal component analysis’s first k components to make the meta-dataset columns coherent and trained machine learning classifiers to predict the best fusion architecture. In this paper, we take a new route to build the meta-dataset. We use the Sequential Forward Floating Selection algorithm and a T transform to reduce the features and match them to a given number, respectively. Our findings indicate that our proposed method could improve the accuracy in predicting the best sensor fusion architecture for multiple domains.
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Data fusion efforts are expected to result in an improvement in predictive power and therefore provide more reliable results in potentially low validity settings.
“The expectation in machine learning is that data fusion efforts will result in an improvement in predictive power17,18 and therefore provide more reliable results in potentially low validity settings19.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [17]
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- Authors
- Molino-Minero-Re, E., Aguileta, A. A., Brena, R. F., & Garcia-Ceja, E.
- Affiliations
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas—Unidad Yucatán, Universidad Nacional Autónoma de México, Sierra Papacal, Yucatán 97302, Mexico
- Autonomous University of Yucatán (MX)
- Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, NL 64849, Mexico
- Optimeering (Norway) (NO)
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-10-22
- Volume
- 21
- Issue
- 21
- Pages
- 7007
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8586946
- OpenAlex
- W3210833372
- Cited by
- 11
- References
- 50
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 2 · 2023: 3 · 2022: 1
- FWCI
- 0.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- PAPIIT-DGAPA (UNAM) (103420)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Sensor Networks and Detection Algorithms
- Keywords
- sensor fusion architecture, multi-sensor fusion, feature selection, sequential floating forward selection, dimensionality reduction, principal component analysis, domain generalization
- MeSH terms
- Machine Learning, Algorithms, Reproducibility of Results
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