Title :
Analysis of active feature selection in optic nerve data using labeled fuzzy C-means clustering
Author :
Park, Jong-Min ; Yae, Hyae-Duk
Author_Institution :
Dept. of Electr. & Comput. Eng., San Diego State Univ., CA, USA
fDate :
6/24/1905 12:00:00 AM
Abstract :
Describes an iterative analysis technique that aids in the process of searching for an optimal set of features for classification, and its application to detection of early glaucoma from optic nerve data in an evolving data acquisition system. The selection and evaluation of features were done using fuzzy C-means clustering and support vector machines. The clustering method was updated using a semi-supervised process. The search space for feature selection was reduced using an active feature selection algorithm. Data samples from different stages of the evolving system are analyzed and evaluated
Keywords :
eye; feature extraction; fuzzy set theory; iterative methods; laser applications in medicine; learning (artificial intelligence); learning automata; neural nets; patient diagnosis; pattern classification; pattern clustering; polarimetry; active feature selection; classification; early glaucoma; evolving data acquisition system; iterative analysis technique; labeled fuzzy C-means clustering; optic nerve data; search space; semi-supervised process; support vector machines; Data mining; Diseases; Feature extraction; Hardware; Machine learning; Nerve fibers; Optical sensors; Pattern analysis; Pattern classification; Testing;
Conference_Titel :
Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7280-8
DOI :
10.1109/FUZZ.2002.1006742
