Core Concepts
Feature reselection using REFRESH efficiently improves secondary model performance characteristics.
Abstract
1. Introduction
Feature selection is crucial in machine learning models, but additional characteristics like fairness and robustness are important.
Deployed models need to be corrected for responsible artificial intelligence characteristics.
Introducing the problem of feature reselection to select features with respect to secondary model performance characteristics efficiently.
2. Data Extraction
"Empirical evaluations on three datasets, including a large-scale loan defaulting dataset show that REFRESH can help find alternate models with better model characteristics efficiently."
3. Quotations
"REFRESH’s underlying algorithm is a novel technique using SHAP values and correlation analysis that can approximate for the predictions of a model without having to train these models."
4. Further Questions
How can REFRESH be applied in other industries beyond finance?
What potential drawbacks or limitations might arise from using SHAP values for feature reselection?
How can the concept of feature reselection impact the future development of machine learning algorithms?
Stats
複数のデータセットでの実証評価により、REFRESHはモデル特性を効率的に向上させることが示されました。
Quotes
"REFRESH’s underlying algorithm is a novel technique using SHAP values and correlation analysis that can approximate for the predictions of a model without having to train these models."