DeepAI AI Chat
Log In Sign Up

Optimizing Data Shapley Interaction Calculation from O(2^n) to O(t n^2) for KNN models

by   Mohamed Karim Belaid, et al.

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.


page 17

page 18


Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (1990)

This is the Proceedings of the Sixth Conference on Uncertainty in Artifi...

A Topological Approach to Measuring Training Data Quality

Data quality is crucial for the successful training, generalization and ...

Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

Given a data set D containing millions of data points and a data consume...

An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms

This paper focuses on valuating training data for supervised learning ta...

Regularization of Kriging interpolation on irregularly spaced data

Interpolation models are critical for a wide range of applications, from...

LAVA: Data Valuation without Pre-Specified Learning Algorithms

Traditionally, data valuation is posed as a problem of equitably splitti...