Abstract
A method for enhancing the performance of feature selection algorithms is proposed. The proposed method is a two step process - first a feature subset is selected with optimum mutual information content and then this subset is searched to find a smaller subset, which has the best separability between classes. A subset with "optimum" mutual information content is the one which contains most of the information that is present in the rest of set. An expression has been derived to find such a subset efficiently. The two-step process is shown to reduce the search space drastically. The method is implemented with a simple Genetic Algorithm (SGA) and tested using hyperspectral remote-sensing images (acquired by AVIRIS sensor) as a data set. Theoretical result shows that the proposed method reduces the computation load by 90%. A computational efficiency to the order ̃20% is obtained on the implementation of proposed method with SGA. The method is sufficiently general to be used to enhance other feature selection algorithms.
| Original language | English (US) |
|---|---|
| Pages | 3264-3267 |
| Number of pages | 4 |
| State | Published - Dec 1 2004 |
| Event | 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 - Anchorage, AK, United States Duration: Sep 20 2004 → Sep 24 2004 |
Other
| Other | 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 |
|---|---|
| Country | United States |
| City | Anchorage, AK |
| Period | 9/20/04 → 9/24/04 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Earth and Planetary Sciences(all)
Cite this
}
Enhancing the performance of feature selection algorithms for classifying hyperspectral imagery. / Kumar, Mukesh; Duffy, Christopher J.; Reed, Patrick M.
2004. 3264-3267 Paper presented at 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004, Anchorage, AK, United States.Research output: Contribution to conference › Paper
TY - CONF
T1 - Enhancing the performance of feature selection algorithms for classifying hyperspectral imagery
AU - Kumar, Mukesh
AU - Duffy, Christopher J.
AU - Reed, Patrick M.
PY - 2004/12/1
Y1 - 2004/12/1
N2 - A method for enhancing the performance of feature selection algorithms is proposed. The proposed method is a two step process - first a feature subset is selected with optimum mutual information content and then this subset is searched to find a smaller subset, which has the best separability between classes. A subset with "optimum" mutual information content is the one which contains most of the information that is present in the rest of set. An expression has been derived to find such a subset efficiently. The two-step process is shown to reduce the search space drastically. The method is implemented with a simple Genetic Algorithm (SGA) and tested using hyperspectral remote-sensing images (acquired by AVIRIS sensor) as a data set. Theoretical result shows that the proposed method reduces the computation load by 90%. A computational efficiency to the order ̃20% is obtained on the implementation of proposed method with SGA. The method is sufficiently general to be used to enhance other feature selection algorithms.
AB - A method for enhancing the performance of feature selection algorithms is proposed. The proposed method is a two step process - first a feature subset is selected with optimum mutual information content and then this subset is searched to find a smaller subset, which has the best separability between classes. A subset with "optimum" mutual information content is the one which contains most of the information that is present in the rest of set. An expression has been derived to find such a subset efficiently. The two-step process is shown to reduce the search space drastically. The method is implemented with a simple Genetic Algorithm (SGA) and tested using hyperspectral remote-sensing images (acquired by AVIRIS sensor) as a data set. Theoretical result shows that the proposed method reduces the computation load by 90%. A computational efficiency to the order ̃20% is obtained on the implementation of proposed method with SGA. The method is sufficiently general to be used to enhance other feature selection algorithms.
UR - http://www.scopus.com/inward/record.url?scp=15944423916&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=15944423916&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:15944423916
SP - 3264
EP - 3267
ER -