@inproceedings{f96321b8818a4650942c2df617f8e035,
title = "Measuring the Stability of Feature Selection",
abstract = "In feature selection algorithms, “stability” is the sensitivity of the chosen feature set to variations in the supplied training data. As such it can be seen as an analogous concept to the statistical variance of a predictor. However unlike variance, there is no unique definition of stability, with numerous proposed measures over 15 years of literature. In this paper, instead of defining a new measure, we start from an axiomatic point of view and identify what properties would be desirable. Somewhat surprisingly, we find that the simple Pearson{\textquoteright}s correlation coefficient has all necessary properties, yet has somehow been overlooked in favour of more complex alternatives. Finally, we illustrate how the use of this measure in practice can provide better interpretability and more confidence in the model selection process. The data and software related to this paper are available at https://github.com/nogueirs/ECML2016.",
author = "Sarah Nogueira and Gavin Brown",
year = "2016",
month = sep,
day = "19",
doi = "10.1007/978-3-319-46227-1_28",
language = "English",
isbn = "978-3-319-46127-4",
series = "Lecture Notes in Artificial Intelligence ",
publisher = "Springer Nature",
pages = "442--457",
editor = "Paolo Frasconi and Niels Landwehr and Giuseppe Manco and Jilles Vreeken",
booktitle = "European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I",
address = "United States",
note = "European Conference, ECML PKDD ; Conference date: 19-09-2016 Through 23-09-2016",
}