- Tytuł:
- CMIM-2: An Enhanced Conditional Mutual Information Maximization Criterion for Feature Selection
- Autorzy:
-
Vergara, J. R.
Estévez, P. A. - Powiązania:
- https://bibliotekanauki.pl/articles/108783.pdf
- Data publikacji:
- 2010
- Wydawca:
- Społeczna Akademia Nauk w Łodzi
- Tematy:
-
feature selection
conditional mutual information
information theory
relevance
redundancy - Opis:
- A new greedy feature selection criterion is proposed as an enhancement of the conditional mutual information maximization criterion (CMIM). The new criterion, called CMIM-2, allows detecting relevant features that are complementary in the class prediction better than the original criterion. In addition, we present a methodology to approximate the conditional mutual information to spaces of three variables, avoiding its estimation in high-dimensional spaces. Experimental results for artificial and UCI benchmark datasets show that the proposed criterion outperforms the original CMIM criterion.
- Źródło:
-
Journal of Applied Computer Science Methods; 2010, 2 No. 1; 5-20
1689-9636 - Pojawia się w:
- Journal of Applied Computer Science Methods
- Dostawca treści:
- Biblioteka Nauki
Artykuł