Enhancing Sensor Data Imputation: OWA-Based Model Aggregation for Missing Values

Muthana Al-Amidie, Laith Alzubaidi (), Muhammad Aminul Islam and Derek T. Anderson
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Muthana Al-Amidie: Department of Electrical Engineering, University of Babylon, Babylon, Hilla 51001, Iraq
Laith Alzubaidi: School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
Muhammad Aminul Islam: Department of Electrical and Computer Engineering & Computer Science, University of New Haven, West Haven, CT 06516, USA
Derek T. Anderson: Department of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211, USA

Future Internet, 2024, vol. 16, issue 6, 1-16

Abstract: 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.

Keywords: missing data imputation; quadratic programming; OWA operators; data aggregation; measurement learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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