{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:09:16Z","timestamp":1760144956694,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Due to some limitations in the data collection process caused either by human-related errors or by collection electronics, sensors, and network connectivity-related errors, the important values at some points could be lost. However, a complete dataset is required for the desired performance of the subsequent applications in various fields like engineering, data science, statistics, etc. An efficient data imputation technique is desired to fill in the missing data values to achieve completeness within the dataset. The fuzzy integral is considered one of the most powerful techniques for multi-source information fusion. It has a wide range of applications in many real-world decision-making problems that often require decisions to be made with partially observable\/available information. To address this problem, algorithms impute missing data with a representative sample or by predicting the most likely value given the observed data. In this article, we take a completely different approach to the information fusion task in the ordered weighted averaging (OWA) context. In particular, we empirically explore for different distributions how the weights\/importance of the missing sources are distributed across the observed inputs\/sources. The experimental results on the synthetic and real-world datasets demonstrate the applicability of the proposed methods.<\/jats:p>","DOI":"10.3390\/fi16060193","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T03:46:49Z","timestamp":1717127209000},"page":"193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Sensor Data Imputation: OWA-Based Model Aggregation for Missing Values"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8116-1871","authenticated-orcid":false,"given":"Muthana","family":"Al-Amidie","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, University of Babylon, Babylon, Hilla 51001, Iraq"},{"name":"Department of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7296-5413","authenticated-orcid":false,"given":"Laith","family":"Alzubaidi","sequence":"additional","affiliation":[{"name":"School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia"},{"name":"Research and Development Department, Akunah Company, Brisbane, QLD 4120, Australia"}]},{"given":"Muhammad Aminul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering & Computer Science, University of New Haven, West Haven, CT 06516, USA"}]},{"given":"Derek T.","family":"Anderson","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121428","DOI":"10.1016\/j.eswa.2023.121428","article-title":"A missing manufacturing process data imputation framework for nonlinear dynamic soft sensor modeling and its application","volume":"237","author":"Ma","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.future.2021.09.033","article-title":"Handling missing data in near real-time environmental monitoring: A system and a review of selected methods","volume":"128","author":"Zhang","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"114425","DOI":"10.1016\/j.eswa.2020.114425","article-title":"RESI: A region-splitting imputation method for different types of missing data","volume":"168","author":"Peng","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"127799","DOI":"10.1016\/j.energy.2023.127799","article-title":"An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction","volume":"278","author":"Sareen","year":"2023","journal-title":"Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1109\/TFUZZ.2021.3058643","article-title":"Hybrid missing value imputation algorithms using fuzzy c-means and vaguely quantified rough set","volume":"30","author":"Li","year":"2021","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1109\/91.784206","article-title":"A fuzzy k-modes algorithm for clustering categorical data","volume":"7","author":"Huang","year":"1999","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4189","DOI":"10.1109\/TITS.2022.3233890","article-title":"Traffic prediction with missing data: A multi-task learning approach","volume":"24","author":"Wang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s10182-022-00461-9","article-title":"Multiple imputation of ordinal missing not at random data","volume":"107","author":"Hammon","year":"2023","journal-title":"AStA Adv. Stat. Anal."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Little, R.J., and Rubin, D.B. (2019). Statistical Analysis with Missing Data, John Wiley & Sons.","DOI":"10.1002\/9781119482260"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Islam, M.A., Anderson, D.T., Petry, F., Smith, D., and Elmore, P. (2017, January 9\u201312). The fuzzy integral for missing data. Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy.","DOI":"10.1109\/FUZZ-IEEE.2017.8015475"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, C., Qin, Y., Zhu, X., Zhang, J., and Zhang, S. (2006, January 16\u201318). Clustering-based missing value imputation for data preprocessing. Proceedings of the 2006 4th IEEE International Conference on Industrial Informatics, Singapore.","DOI":"10.1109\/INDIN.2006.275767"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2895","DOI":"10.1016\/j.atmosenv.2004.02.026","article-title":"Methods for imputation of missing values in air quality data sets","volume":"38","author":"Junninen","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1080\/713827170","article-title":"A pre-processing method to deal with missing values by integrating clustering and regression techniques","volume":"17","author":"Tseng","year":"2003","journal-title":"Appl. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1175\/1520-0442(2001)014<0853:AOICDE>2.0.CO;2","article-title":"Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values","volume":"14","author":"Schneider","year":"2001","journal-title":"J. Clim."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1080\/03081079608945123","article-title":"Modelling decision making using immediate probabilities","volume":"24","author":"Engemann","year":"1996","journal-title":"Int. J. Gen. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1002\/int.21838","article-title":"A new ordered weighted averaging operator to obtain the associated weights based on the principle of least mean square errors","volume":"32","author":"Bai","year":"2017","journal-title":"Int. J. Intell. Syst."},{"key":"ref_17","first-page":"15","article-title":"Fuzzy Generalized Hybrid Aggregation Operators and its Application in Fuzzy Decision Making","volume":"12","author":"Merigo","year":"2010","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106629","DOI":"10.1016\/j.cie.2020.106629","article-title":"An integrated approach towards modeling ranked weights","volume":"147","author":"Liu","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1093\/ije\/dyad008","article-title":"Assumptions and analysis planning in studies with missing data in multiple variables: Moving beyond the MCAR\/MAR\/MNAR classification","volume":"52","author":"Lee","year":"2023","journal-title":"Int. J. Epidemiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e2407","DOI":"10.1002\/icd.2407","article-title":"Best practices for addressing missing data through multiple imputation","volume":"33","author":"Woods","year":"2024","journal-title":"Infant Child Dev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1049\/iet-com.2018.5276","article-title":"Spectrum sensing based on Bayesian generalised likelihood ratio for cognitive radio systems with multiple antennas","volume":"13","author":"Micheas","year":"2019","journal-title":"IET Commun."},{"key":"ref_22","first-page":"9071","article-title":"Long-term missing value imputation for time series data using deep neural networks","volume":"35","author":"Park","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"120201","DOI":"10.1016\/j.eswa.2023.120201","article-title":"Deep learning versus conventional methods for missing data imputation: A review and comparative study","volume":"227","author":"Sun","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Honghai, F., Guoshun, C., Cheng, Y., Bingru, Y., and Yumei, C. (2005, January 14\u201316). A SVM regression based approach to filling in missing values. Proceedings of the International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Melbourne, Australia.","DOI":"10.1007\/11553939_83"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s10115-022-01769-3","article-title":"Manifold clustering optimized by adaptive aggregation strategy","volume":"65","author":"Zhang","year":"2023","journal-title":"Knowl. Inf. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/21.87068","article-title":"On ordered weighted averaging aggregation operators in multicriteria decisionmaking","volume":"18","author":"Yager","year":"1988","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/TFUZZ.2007.902041","article-title":"Continuous preference logic for system evaluation","volume":"15","author":"Dujmovic","year":"2007","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TFUZZ.2003.822679","article-title":"Quantitative weights and aggregation","volume":"12","author":"Calvo","year":"2004","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1016\/j.ins.2008.11.013","article-title":"The induced generalized OWA operator","volume":"179","year":"2009","journal-title":"Inf. Sci."},{"key":"ref_30","unstructured":"Grabisch, M., and Sugeno, M. (1992, January 8\u201312). Multi-attribute classification using fuzzy integral. Proceedings of the [1992 Proceedings] IEEE International Conference on Fuzzy Systems, San Diego, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Anderson, D.T., Price, S.R., and Havens, T.C. (2014, January 6\u201311). Regularization-based learning of the choquet integral. Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China.","DOI":"10.1109\/FUZZ-IEEE.2014.6891630"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/21.364825","article-title":"Combining multiple neural networks by fuzzy integral for robust classification","volume":"25","author":"Cho","year":"1995","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_33","unstructured":"Waugh, S., and Adams, A. (December, January 27). Pruning within cascade-correlation. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/6\/193\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:51:31Z","timestamp":1760107891000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/6\/193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,31]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["fi16060193"],"URL":"https:\/\/doi.org\/10.3390\/fi16060193","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2024,5,31]]}}}