@inproceedings{efde641f510f41ae90ecaed5ca845d04,
title = "Finansal Veri Setleri {\"U}zerinde {\"O}nerilen Hibrit {\"O}znitelik S{\v e}im Y{\"o}ntemi",
abstract = "One of the most important problems encountered in data analysis is the removal of variables in the data that create noise and affect the solution negatively. The most important method used to solve this problem is feature selection. In the paper, a hybrid method is proposed for feature selection on financial data. In the literature, feature selection is examined in 3 categories: filtering, wrapper and recursive methods. In the proposed hybrid method, 2 filtering, 2 buried and 1 spiral method are utilized. As a result of the studies on 2 different financial data, the proposed method successes as good results as the best methods in the literature. The study showed no single feature selection method to use for each data set. In addition, scaling in accordance with the data increased the success rate.",
keywords = "Feature selection, machine learning, recursive feature elimination, scaling",
author = "Mustafa Yildirim and Suat Ozdemir",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th International Conference on Computer Science and Engineering, UBMK 2019 ; Conference date: 11-09-2019 Through 15-09-2019",
year = "2019",
month = sep,
doi = "10.1109/UBMK.2019.8906987",
language = "T{\"u}rk{\c c}e",
series = "UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "429--434",
booktitle = "UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering",
address = "!!United States",
}