A survey on machine learning for data fusion

By Tong Meng, Xuyang Jing, Zheng Yan, Witold Pedrycz. Information Fusion, 2020

596 citations recorded by the source corpus.

Abstract Data fusion is a prevalent way to deal with imperfect raw data for capturing reliable, valuable and accurate information. Comparing with a range of classical probabilistic data fusion techniques, machine learning method that automatically learns from past experiences without explicitly programming, remarkably renovates fusion techniques by offering the strong ability of computing and predicting. Nevertheless, the literature still lacks a thorough review of the recent advances of machine learning for data fusion. Therefore, it is beneficial to review and summarize the state of the art in order to gain a deep insight on how machine learning can benefit and optimize data fusion. In this paper, we provide a comprehensive survey on data fusion methods based on machine learning. We first offer a detailed introduction to the background of data fusion and machine learning in terms of definitions, applications, architectures, processes, and typical techniques. Then, we propose a number of requirements and employ them as criteria to review and evaluate the performance of existing fusion methods based on machine learning. Through the literature review, analysis and comparison, we finally come up with a number of open issues and propose future research directions in this field.

Subfields: Artificial Intelligence, Building and Construction, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/cf15c1898c81594165d74de465605aa9f559c769

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In Artificial Intelligence

Citations in the corpus

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “multimodal fusion strategies [36] are the two key components of multimodal affective analysis systems, which often outperform the unimodal emotion recognition systems [37].”
Deep Multimodal Data Fusion (2024, background): “Compared to the existing surveys [2, 4, 45, 46, 121, 128, 227, 235, 243] that usually focus on one single task (such as multimodal object recognition) with one specific combination of two modalities (such as RGB+depth data), this survey owns a broader scope covering various modalities and their…”
Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “Ref Animal Input Data Functionality Models/Algorithms Best Output [411] Poultry Air quality data Early prediction of Coccidiosis in poultry farms KNN AUC = 0.897–0.967 [412] Cattle On-farm questionnaires, clinical and milk records Prediction of mastitis infection in dairy herds RF CONT vs. ENV: Acc ”
Smart Anomaly Detection in Sensor Systems (2020, methodology): “This is typically the realm of data fusion [116], which has recently turned its attention to smart methods in IoT set-”
Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision (2020, methodology): “The approaches can be roughly divided into techniques fusing information on raw data/input level, on feature/intermediate level, or on decision/output level [210].”