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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007661
