- Tytuł:
- The Monte Carlo feature selection and interdependency discovery is unbiased
- Autorzy:
-
Dramiński, M.
Kierczak, M.
Nowak-Brzezińska, A.
Koronecki, J.
Komorowski, J. - Powiązania:
- https://bibliotekanauki.pl/articles/205575.pdf
- Data publikacji:
- 2011
- Wydawca:
- Polska Akademia Nauk. Instytut Badań Systemowych PAN
- Tematy:
-
supervised classification
feature selection
feature interactions
high-dimensional problems
applications to genomic and proteomic data - Opis:
- We show that the Monte Carlo feature selection algorithm for supervised classification proposed, by Dramiński et al. (2008), is not biased towards features with many categories (levels or values). While the algorithm, later extended to include the functionality of discovering interdependencies between features, is surprisingly simple and has been successfully used on many biological data and transactional data of commercial origin, and it has never revealed any bias of the type mentioned, the alleged property of its unbiasedness required a closer scrutiny which is thus provided here. Admittedly, the algorithm does reveal some bias coming from another source, but it is negligible. Hence our final claim is that the algorithm is practically unbiased and the results it provides can be considered fully reliable.
- Źródło:
-
Control and Cybernetics; 2011, 40, 2; 199-211
0324-8569 - Pojawia się w:
- Control and Cybernetics
- Dostawca treści:
- Biblioteka Nauki
Artykuł