@inproceedings{69159e979f484011a70f2f27cee89066,
title = "Stability Evaluation of Computational Intelligence-Based Subset Feature Selection Methods on Breast Cancer Data Analysis",
abstract = "The stability of computational intelligence based subset feature selection (CI-SFS) has not been explored. In this study, 44 methods are evaluated on BCDR-F03 using 5 stability estimators. Experimental results identify 3 methods achieving 0.55 or higher scores from two estimators, 7 methods leading to good classification (area under the curve ≥ 0.80) and 4 potential signatures helping cancer diagnosis. Conclusively, most of the CI-SFS methods seem sensitive to data perturbation and different estimators cause inconsistent results. In future work, attention should be paid to developing robust fitness functions to enhance feature preference and designing advanced estimators to quantify the feature selection stability.",
keywords = "Stability, breast cancer diagnosis, computational intelligence, signature discovery, subset feature selection",
author = "Shaode Yu and Boji Liu and Bingjie Li 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/FAIA220579",
language = "English (US)",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "587--594",
editor = "Tallon-Ballesteros, {Antonio J.}",
booktitle = "Proceedings of CECNet 2022 - 12th International Conference on Electronics, Communications and Networks, CECNet 2022",
}