DocumentCode
1682881
Title
Active feature selection in optic nerve data using support vector machine
Author
Park, Jong-Min ; Reed, Jerry ; Zhou, Qienyuan
Author_Institution
Dept. of Electr. & Comput. Eng., San Diego State Univ., CA, USA
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1178
Lastpage
1182
Abstract
Describes a data mining framework that aids in the process of finding an optimal set of features and its application into classification and detection of glaucoma from optic nerve data. The selection and evaluation of features were done using support vector machines. The search space for feature selection were reduced using an active feature sampling algorithm
Keywords
data mining; eye; feature extraction; laser applications in medicine; learning (artificial intelligence); learning automata; medical image processing; patient diagnosis; active feature selection; active learning; data mining; glaucoma classification; glaucoma detection; image processing; optic nerve data; support vector machine; Data mining; Diseases; Feature extraction; Hardware; Machine learning; Optical sensors; Pattern analysis; Pattern classification; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007661
Filename
1007661
Link To Document