Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction Model
As sensor investment decisions become increasingly crucial across industries, the challenge of optimal sensor selection has gained prominence. Traditional approaches primarily address this problem from a performance-oriented perspective, focusing on metrics like prediction or estimation accuracy. Ho...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11003998/ |
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| author | Jeong-Gi Lee Deok-Joo Lee |
| author_facet | Jeong-Gi Lee Deok-Joo Lee |
| author_sort | Jeong-Gi Lee |
| collection | DOAJ |
| description | As sensor investment decisions become increasingly crucial across industries, the challenge of optimal sensor selection has gained prominence. Traditional approaches primarily address this problem from a performance-oriented perspective, focusing on metrics like prediction or estimation accuracy. However, these methods often overlook the economic value that sensors can contribute. This paper addresses this gap by proposing a framework for evaluating sensors based on their economic contributions. Our approach leverages cooperative game theory and integrates explainable AI (XAI) techniques for feature valuation to assess the impact of each sensor within prediction models. We introduce two valuation methods—Marginal Influence Between Models (MIBM) and Marginal Influence Within Models (MIWM)—and provide guidelines on when to apply each method depending on the scenario. The MIWM method is found to be more effective in capturing the interactions and synergies among sensors, although it comes with higher computational costs. On the other hand, MIBM is advantageous in scenarios where computational efficiency and robustness are prioritized. To support method selection, we also analyze the computational complexity of both approaches and derive error bounds. Through numerical experiments using real-world semiconductor manufacturing data, we demonstrate that sensor selection based on economic value can lead to significantly different outcomes compared to conventional methods. Our findings show the importance of considering both economic benefit and cost factors in sensor selection, particularly in applications involving multiple sensor clusters or heterogeneous data sources. |
| format | Article |
| id | doaj-art-49c46d5ba3674bde8d0fa2b931571219 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-49c46d5ba3674bde8d0fa2b9315712192025-08-20T03:12:27ZengIEEEIEEE Access2169-35362025-01-0113868128682710.1109/ACCESS.2025.356996211003998Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction ModelJeong-Gi Lee0https://orcid.org/0000-0001-6105-6604Deok-Joo Lee1https://orcid.org/0000-0002-9795-8199Department of Industrial Engineering, Seoul National University, Seoul, Republic of KoreaDepartment of Industrial Engineering, Seoul National University, Seoul, Republic of KoreaAs sensor investment decisions become increasingly crucial across industries, the challenge of optimal sensor selection has gained prominence. Traditional approaches primarily address this problem from a performance-oriented perspective, focusing on metrics like prediction or estimation accuracy. However, these methods often overlook the economic value that sensors can contribute. This paper addresses this gap by proposing a framework for evaluating sensors based on their economic contributions. Our approach leverages cooperative game theory and integrates explainable AI (XAI) techniques for feature valuation to assess the impact of each sensor within prediction models. We introduce two valuation methods—Marginal Influence Between Models (MIBM) and Marginal Influence Within Models (MIWM)—and provide guidelines on when to apply each method depending on the scenario. The MIWM method is found to be more effective in capturing the interactions and synergies among sensors, although it comes with higher computational costs. On the other hand, MIBM is advantageous in scenarios where computational efficiency and robustness are prioritized. To support method selection, we also analyze the computational complexity of both approaches and derive error bounds. Through numerical experiments using real-world semiconductor manufacturing data, we demonstrate that sensor selection based on economic value can lead to significantly different outcomes compared to conventional methods. Our findings show the importance of considering both economic benefit and cost factors in sensor selection, particularly in applications involving multiple sensor clusters or heterogeneous data sources.https://ieeexplore.ieee.org/document/11003998/Sensor data valuationsensor selectionfeature valuationeconomic evaluation of sensor |
| spellingShingle | Jeong-Gi Lee Deok-Joo Lee Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction Model IEEE Access Sensor data valuation sensor selection feature valuation economic evaluation of sensor |
| title | Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction Model |
| title_full | Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction Model |
| title_fullStr | Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction Model |
| title_full_unstemmed | Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction Model |
| title_short | Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction Model |
| title_sort | economic evaluation of the investment in sensor equipment based on data valuation in prediction model |
| topic | Sensor data valuation sensor selection feature valuation economic evaluation of sensor |
| url | https://ieeexplore.ieee.org/document/11003998/ |
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