Shapley-Based Data Valuation Method for the Machine Learning Data Markets (MLDM)

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Baghcheband, H (Author) Other; Carlos Soares (Author) FEUP; reis, lp (Author) FEUP

Conference proceedings International

Title: FOUNDATIONS OF INTELLIGENT SYSTEMS, ISMIS 2024

Pages: 170-177

27th International Symposium on Methodologies for Intelligent Systems (ISMIS)

Poitiers, FRANCE, JUN 17-19, 2024

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Other information

Authenticus ID: P-010-NB7

DOI: 10.1007/978-3-031-62700-2_16

Abstract (EN): Data valuation, the process of assigning value to data based on its utility and usefulness, is a critical and largely unexplored aspect of data markets. Within the Machine Learning Data Market (MLDM), a platform that enables data exchange among multiple agents, the challenge of quantifying the value of data becomes particularly prominent. Agents within MLDM are motivated to exchange data based on its potential impact on their individual performance. Shapley Value-based methods have gained traction in addressing this challenge, prompting our study to investigate their effectiveness within the MLDM context. Specifically, we propose the Gain Data Shapley Value (GDSV) method tailored for MLDM and compare it to the original data valuation method used in MLDM. Our analysis focuses on two common learning algorithms, Decision Tree (DT) and K-nearest neighbors (KNN), within a simulated society of five agents, tested on 45 classification datasets. results show that the GDSV leads to incremental improvements in predictive performance across both DT and KNN algorithms compared to performance-based valuation or the baseline. These findings underscore the potential of Shapley Value-based methods in identifying high-value data within MLDM while indicating areas for further improvement.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8