Learning ECOC Code Matrix for Multiclass Classification with Application to Glaucoma Diagnosis

Journal of Medical Systems
Xiaolong BaiPaul T Chew

Abstract

Classification of different mechanisms of angle closure glaucoma (ACG) is important for medical diagnosis. Error-correcting output code (ECOC) is an effective approach for multiclass classification. In this study, we propose a new ensemble learning method based on ECOC with application to classification of four ACG mechanisms. The dichotomizers in ECOC are first optimized individually to increase their accuracy and diversity (or interdependence) which is beneficial to the ECOC framework. Specifically, the best feature set is determined for each possible dichotomizer and a wrapper approach is applied to evaluate the classification accuracy of each dichotomizer on the training dataset using cross-validation. The separability of the ECOC codes is maximized by selecting a set of competitive dichotomizers according to a new criterion, in which a regularization term is introduced in consideration of the binary classification performance of each selected dichotomizer. The proposed method is experimentally applied for classifying four ACG mechanisms. The eye images of 152 glaucoma patients are collected by using anterior segment optical coherence tomography (AS-OCT) and then segmented, from which 84 features are extracted. The weighted...Continue Reading

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Citations

May 22, 2016·Computer Methods and Programs in Biomedicine·Swamidoss Issac NiwasPaul T K Chew
Feb 22, 2021·Methods : a Companion to Methods in Enzymology·Farhat AfzaD Jude Hemanth
May 1, 2021·Methods : a Companion to Methods in Enzymology·Jiancheng AnRui Cao
Jun 22, 2021·Methods : a Companion to Methods in Enzymology·Yuan LiuLipo Wang

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