An open-access research monograph by Marek Gagolewski (download PDF)
A proper fusion of complex data is of interest to many researchers in diverse fields, including computational statistics, computational geometry, bioinformatics, machine learning, pattern recognition, quality management, engineering, statistics, finance, economics, etc. It plays a crucial role in:
- synthetic description of data processes or whole domains,
- creation of rule bases for approximate reasoning tasks,
- reaching consensus and selection of the optimal strategy in decision support systems,
- imputation of missing values,
- data deduplication and consolidation,
- record linkage across heterogeneous databases,
- clustering.
Furthermore, many useful machine learning methods are based on a proper aggregation of information entities. In particular, the class of ensemble methods for classification is very successful because of the assumption that no single "weak" classifier can perform as nicely as the whole group. Neural networks and other deep learning tools can be understood as hierarchies of individual fusion functions. Appropriate data fusion is crucial for privacy reasons as well (think: GDPR).
This open-access research monograph integrates the spread-out results from different domains using the methodology of the well-established classical aggregation framework, introduces researchers and practitioners to Aggregation 2.0, as well as points out the challenges and interesting directions for further research.
Gagolewski M., Data Fusion: Theory, Methods, and Applications, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, 2015, 290 pp., ISBN: 978-83-63159-20-7, DOI: 10.5281/zenodo.6960306.
Reviewers: Gleb Beliakov and Radko Mesiar.