Optimizing Data Shapley Interaction Calculation From $O(2^n)$ to $O(t N^2)$ for KNN Models

MCML Authors

Eyke Hüllermeier

Prof. Dr.

Principal Investigator

Artificial Intelligence and Machine Learning

Abstract

With the rapid growth of data availability and usage, quantifying the added value of each training data point has become a crucial process in the field of artificial intelligence. The Shapley values have been recognized as an effective method for data valuation, enabling efficient training set summarization, acquisition, and outlier removal. In this paper, we introduce 'STI-KNN', an innovative algorithm that calculates the exact pair-interaction Shapley values for KNN models in $O(t n^2)$ time, which is a significant improvement over the $O(2^n)$ time complexity of baseline methods. By using STI-KNN, we can efficiently and accurately evaluate the value of individual data points, leading to improved training outcomes and ultimately enhancing the effectiveness of artificial intelligence applications.

misc

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Preprint

Apr. 2023

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Authors

M. K. Belaid • D. E. Mekki • M. Rabus • E. Hüllermeier

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Links

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Research Area

 A3 | Computational Models