Abstract
Determining a good feature set is critical to the performance of learning algorithms such as classifiers. Recently, researchers have proposed evolutionary-based feature extraction methods that aim to find a good feature set by combining the original features with new features generated by mathematical transformations of the original features. In this paper, we propose dynamically collecting past performance information on promising features and operators to use in our mutation method. We consider how to make our evolutionary algorithm more efficient and reliable by reducing overfitting. Preliminary results using UCI data show that our dynamic mutation method only slightly enhances the classification accuracy but it produces more reliable results.
| Original language | English (US) |
|---|---|
| Title of host publication | 2011 IEEE Congress of Evolutionary Computation, CEC 2011 |
| Pages | 409-416 |
| Number of pages | 8 |
| DOIs | |
| State | Published - 2011 |
| Event | 2011 IEEE Congress of Evolutionary Computation, CEC 2011 - New Orleans, LA, United States Duration: Jun 5 2011 → Jun 8 2011 |
Other
| Other | 2011 IEEE Congress of Evolutionary Computation, CEC 2011 |
|---|---|
| Country | United States |
| City | New Orleans, LA |
| Period | 6/5/11 → 6/8/11 |
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All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Theoretical Computer Science
Cite this
}
Evolutionary based feature extraction with dynamic mutation. / Ahn, Eun Yeong; Mullen, Tracy; Yen, John.
2011 IEEE Congress of Evolutionary Computation, CEC 2011. 2011. p. 409-416 5949647.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Evolutionary based feature extraction with dynamic mutation
AU - Ahn, Eun Yeong
AU - Mullen, Tracy
AU - Yen, John
PY - 2011
Y1 - 2011
N2 - Determining a good feature set is critical to the performance of learning algorithms such as classifiers. Recently, researchers have proposed evolutionary-based feature extraction methods that aim to find a good feature set by combining the original features with new features generated by mathematical transformations of the original features. In this paper, we propose dynamically collecting past performance information on promising features and operators to use in our mutation method. We consider how to make our evolutionary algorithm more efficient and reliable by reducing overfitting. Preliminary results using UCI data show that our dynamic mutation method only slightly enhances the classification accuracy but it produces more reliable results.
AB - Determining a good feature set is critical to the performance of learning algorithms such as classifiers. Recently, researchers have proposed evolutionary-based feature extraction methods that aim to find a good feature set by combining the original features with new features generated by mathematical transformations of the original features. In this paper, we propose dynamically collecting past performance information on promising features and operators to use in our mutation method. We consider how to make our evolutionary algorithm more efficient and reliable by reducing overfitting. Preliminary results using UCI data show that our dynamic mutation method only slightly enhances the classification accuracy but it produces more reliable results.
UR - http://www.scopus.com/inward/record.url?scp=80051972296&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80051972296&partnerID=8YFLogxK
U2 - 10.1109/CEC.2011.5949647
DO - 10.1109/CEC.2011.5949647
M3 - Conference contribution
SN - 9781424478347
SP - 409
EP - 416
BT - 2011 IEEE Congress of Evolutionary Computation, CEC 2011
ER -