@inproceedings{7fbbc73f661c438fa2bafb971496935d,
title = "A Stability Evaluation of Feature Ranking Algorithms on Breast Cancer Data Analysis",
abstract = "Stability of feature preference is a most vital yet rarely explored characteristics of feature ranking algorithms. In this study, 23 feature rankers are evaluated on 4 breast cancer datasets (BCDR-F03, WDBC, GSE10810 and GSE15852) using an advanced stability estimator (S), and 3 rankers are identified showing good stability (S ≥ 0.55) consistently on the four datasets. It suggests that data sufficiency is crucial for the construction of feature importance measure, since more rankers are stable on medical imaging datsets (BCDR-F03 and WDBC) than on gene expression datasets (GSE10810 and GSE15852), and high-dimensional small-sample-size datasets are big challenges of stability estimation. In our future work, more attention should be paid to the topics of developing stable feature ranking algorithms and stability estimators to well tackle different sizes of medical datasets.",
keywords = "Stability, breast cancer, data analysis, feature ranking, matFR",
author = "Shaode Yu and Bingjie Li and Boji Liu and Mingxue Jin and Junjie Wu and Hang Yu",
note = "Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press.; 12th International Conference on Electronics, Communications and Networks, CECNet 2022 ; Conference date: 04-11-2022 Through 07-11-2022",
year = "2022",
month = dec,
day = "13",
doi = "10.3233/FAIA220582",
language = "English (US)",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "606--613",
editor = "Tallon-Ballesteros, {Antonio J.}",
booktitle = "Proceedings of CECNet 2022 - 12th International Conference on Electronics, Communications and Networks, CECNet 2022",
}