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2016 | OriginalPaper | Buchkapitel

Ensemble Classifiers Construction Using Diversity Measures and Random Subspace Algorithm Combination: Application to Glaucoma Diagnosis

verfasst von : Soraya Cheriguene, Nabiha Azizi, Nilanjan Dey

Erschienen in: Medical Imaging in Clinical Applications

Verlag: Springer International Publishing

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Abstract

Glaucoma is a group of eye diseases caused due to excessively high intraocular pressure within the eye. Ensemble classifier construction has attracted increasing interest in the field of pattern recognition and machine learning. Diversity among the classifiers is important factor for each ensemble to be successful. The most widely generation techniques are focused on incorporating the concept of diversity by using different features or training subsets. a classifier selection process becomes an important issue of multiple classifier system by choosing the optimal subset of members that maximizes the performance. The main goal of this study is to develop novel automated glaucoma diagnosis system which analyze and classify retinal images using a novel classification approach based on feature selection and static classifier selection schemes. Experimental results based on RIM-ONE dataset are very encouraging.

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Titel: Ensemble Classifiers Construction Using Diversity Measures and Random Subspace Algorithm Combination: Application to Glaucoma Diagnosis
verfasst von: Soraya Cheriguene
Nabiha Azizi
Nilanjan Dey
Verlag: Springer International Publishing
Buch: Medical Imaging in Clinical Applications
Print ISBN: 978-3-319-33791-3

Electronic ISBN: 978-3-319-33793-7

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-33793-7_6

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