A Bias Correction Algorithm for the Gini Variable Importance Measure in Classification Trees

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Consistent Individualized Feature Attribution for Tree Ensembles (2018, neutral): “Though its motivation is largely heuristic [11], gain is widely used as the basis for feature selection methods [5, 13, 25].”
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data (2009, background): “5 T with an echo time of 135 ms in the pre-therapeutic and postoperative diagnostics of (recurrent) brain tumor (Nhealthy = 153, Ntumor border = 72, Ntumor center = 53) [31,32].”
Fifty Years of Classification and Regression Trees (2014, methodology): “Sandri & Zuccolotto (2008) proposed a method to correct the bias.”
The revival of the Gini importance? (2018, methodology): “We propose a modified approach of that by Sandri and Zuccolotto (2008).”
Explainable AI for Trees: From Local Explanations to Global Understanding (2019, background): “While local explanation methods for trees have not been extensively studied, interpreting tree-based models by assigning a global importance value to each input feature is a well studied problem and many methods have been proposed [20, 19, 61, 69, 70, 27, 2, 40, 31].”