Multi‐layer feature selection incorporating weighted score‐based expert knowledge toward modeling materials with targeted properties
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Date
2020-01-15
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
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‐FSdek, 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. © 2020 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim
Description
This article also appears in:
Progress in Machine Learning. First published: 1 October 2018, Last updated: 8 September 2020
Keywords
Simulation, Algorithms, Automation, Physical properties, Prediction equations, Lithium ion batteries
Citation
Liu, Y., Wu, J.- M., Avdeev, M., Shi, S.- Q. (2020). Multi‐layer feature selection incorporating weighted score‐based expert knowledge toward modeling materials with targeted properties. Advanced Theory and Simulations, 3(2), 1900215. doi:10.1002/adts.201900215