Abstract
Selecting the best group of features from high-dimensional datasets is an important challenge in machine learning. Indeed problems with hundreds of features have now become usual. In the context of filter methods, the selected relevance criterion used for filtering is the key factor of a feature selection method. To select an appropriate criterion among the numerous existing ones, this paper proposes a list of six necessary properties. This paper describes then three relevance criteria, the mutual information, the noise variance and the adjusted R-squared, and compares them in the view of the aforementioned properties. Any new, or popular, criterion could be analysed in the light of these properties.
| Original language | English |
|---|---|
| Title of host publication | Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings |
| Editors | Ignacio Rojas, Gonzalo Joya, Andreu Catala |
| Publisher | Springer Verlag |
| Pages | 579-590 |
| Number of pages | 12 |
| ISBN (Print) | 9783030205171 |
| DOIs | |
| Publication status | Published - 1 Jan 2019 |
| Event | 15th International Work-Conference on Artificial Neural Networks, IWANN 2019 - Gran Canaria, Spain Duration: 12 Jun 2019 → 14 Jun 2019 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11507 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 15th International Work-Conference on Artificial Neural Networks, IWANN 2019 |
|---|---|
| Country | Spain |
| City | Gran Canaria |
| Period | 12/06/19 → 14/06/19 |
Fingerprint
Keywords
- Feature selection
- Regression
- Relevance criteria
Cite this
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About Filter Criteria for Feature Selection in Regression. / Degeest, Alexandra; Verleysen, Michel; Frénay, Benoît.
Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings. ed. / Ignacio Rojas; Gonzalo Joya; Andreu Catala. Springer Verlag, 2019. p. 579-590 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11507 LNCS).Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - About Filter Criteria for Feature Selection in Regression
AU - Degeest, Alexandra
AU - Verleysen, Michel
AU - Frénay, Benoît
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Selecting the best group of features from high-dimensional datasets is an important challenge in machine learning. Indeed problems with hundreds of features have now become usual. In the context of filter methods, the selected relevance criterion used for filtering is the key factor of a feature selection method. To select an appropriate criterion among the numerous existing ones, this paper proposes a list of six necessary properties. This paper describes then three relevance criteria, the mutual information, the noise variance and the adjusted R-squared, and compares them in the view of the aforementioned properties. Any new, or popular, criterion could be analysed in the light of these properties.
AB - Selecting the best group of features from high-dimensional datasets is an important challenge in machine learning. Indeed problems with hundreds of features have now become usual. In the context of filter methods, the selected relevance criterion used for filtering is the key factor of a feature selection method. To select an appropriate criterion among the numerous existing ones, this paper proposes a list of six necessary properties. This paper describes then three relevance criteria, the mutual information, the noise variance and the adjusted R-squared, and compares them in the view of the aforementioned properties. Any new, or popular, criterion could be analysed in the light of these properties.
KW - Feature selection
KW - Regression
KW - Relevance criteria
UR - http://www.scopus.com/inward/record.url?scp=85067548571&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20518-8_48
DO - 10.1007/978-3-030-20518-8_48
M3 - Conference contribution
AN - SCOPUS:85067548571
SN - 9783030205171
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 579
EP - 590
BT - Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings
A2 - Rojas, Ignacio
A2 - Joya, Gonzalo
A2 - Catala, Andreu
PB - Springer Verlag
ER -