A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys.

ABSTRACT: We demonstrate the capabilities of two model-agnostic local post-hoc model interpretability methods, namely breakDown (BD) and shapley (SHAP), to explain the predictions of a black-box classification learning model that establishes a quantitative relationship between chemical composition and multi-principal element alloys (MPEA) phase formation. We trained an ensemble of support vector machines using a dataset with 1,821 instances, 12 features with low pair-wise correlation, and seven phase labels. Feature contributions to the model prediction are computed by BD and SHAP for each composition. The resulting BD and SHAP transformed data are then used as inputs to identify similar composition groups using k-means clustering. Explanation-of-clusters by features reveal that the results from SHA

SUBMITTER: Lee K 

PROVIDER: S-EPMC9270422 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature