Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties

• Published in: Advanced Theory and Simulations
• Main Authors: Liu, Yue, Wu, Jun‐Ming, Avdeev, Maxim, Shi, Si‐Qi
• Other Authors: National Basic Research Program of China
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2020
• Subjects: DML
• Online Access: http://dx.doi.org/10.1002/adts.201900215; https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.201900215; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/adts.201900215

Abstract Selecting proper descriptors or features is one of the central problems in exploring structure–activity relationships of materials using machine learning models. The current feature selection algorithms usually require tedious hyperparameter tuning and do not actively consider the prior knowledge of domain experts about the features. Here, this work proposes a data‐driven multi‐layer feature selection method incorporating domain expert knowledge named DML‐FS dek , which is automated, with users entering training data without manual tuning of the hyperparameters. The domain expert knowledge is quantified by means of weighted scoring and integrated into the selection process to eliminate the risk of crucial features being removed. The test studies on ten material properties datasets demonstrate the potential of the approach to automatically search for a reduced feature set with lower root mean square errors than those for the initial feature set. Essentially, the most relevant material features, the number of which is much smaller than that in the original feature set, are automatically selected to establish a closer and more accurate structure–activity relationship for the materials of interest. As a result, the method represents the targeted properties of materials with a smaller and more interpretable set of features while ensuring equal or better prediction accuracy.