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

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1580

Lastpage

1585

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/FUZZ.2002.1006742

Filename

1006742