@article{
 title = {Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity},
 type = {article},
 year = {2020},
 keywords = {Random Forest,XGBoost,feature importances,feature selection,machine learning,path loss,radio propagation},
 pages = {114-125},
 volume = {1},
 websites = {http://dx.doi.org/10.3390/telecom1020009},
 month = {8},
 publisher = {MDPI AG},
 id = {006b80d1-55bb-390a-ac89-01c1adcdbf72},
 created = {2020-08-31T20:28:48.447Z},
 file_attached = {false},
 profile_id = {c69aa657-d754-373c-91b7-64154b7d5d91},
 last_modified = {2023-02-11T16:55:13.280Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Sotiroudis2020},
 source_type = {article},
 private_publication = {false},
 abstract = {Machine learning models have been widely deployed to tackle the problem of radio propagation. In addition to helping in the estimation of path loss, they can also be used to better understand the details of various propagation scenarios. Our current work exploits the inherent ranking of feature importances provided by XGBoost and Random Forest as a means of indicating the contribution of the underlying propagation mechanisms. A comparison between two different transmitter antenna heights, revealing the associated propagation profiles, is made. Feature selection is then implemented, leading to models with reduced complexity, and consequently reduced training and response times, based on the previously calculated importances.},
 bibtype = {article},
 author = {Sotiroudis, Sotirios P and Goudos, Sotirios K and Siakavara, Katherine},
 doi = {10.3390/telecom1020009},
 journal = {Telecom},
 number = {2}
}

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