Abstract
AbstractOne of the most widely adopted approaches for eXplainable Artificial Intelligence (XAI) involves employing of Shapley values (SVs) to determine the relative importance of input features. While based on a solid mathematical foundation derived from cooperative game theory, SVs have a significant drawback: high computational cost. Calculating the exact SV is an NP-hard problem, necessitating the use of approximations, particularly when dealing with more than twenty features. On the other hand, determining SVs for all features is seldom necessary in practice; users are primarily interested in the most important ones only. This paper introduces the Economic Hierarchical Shapley values (ecoShap) method for calculating SVs for the most crucial features only, with reduced computational cost. EcoShap iteratively expands disjoint groups of features in a tree-like manner, avoiding the expensive computations for the majority of less important features. Our experimental results across eight datasets demonstrate that the proposed technique efficiently identifies top features; at a 50% reduction in computational costs, it can determine between three and seven of the most important features.
Publisher
Springer Nature Switzerland
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