Classification of different mechanisms of @entity2747 ( @entity2747 ) 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 @entity2747 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 @entity2747 mechanisms. The eye images of 152 @entity726 @entity1 are collected by using anterior segment optical coherence tomography (AS-OCT) and then segmented, from which 84 features are extracted. The weighted average classification accuracy of the proposed method is 87.65  % based on the results of leave-one-out cross-validation (LOOCV), which is much better than that of the other existing ECOC methods. The proposed method achieves accurate classification of four @entity2747 mechanisms which is promising to be applied in diagnosis of @entity726 .

Learning ECOC Code Matrix for Multiclass Classification with Application to XXXX .

@entity726 :: ('MESH:D005901', 'Disease') :: ['glaucoma'] @entity1 :: ('9606', 'Species') :: ['patients'] @entity2747 :: ('MESH:D015812', 'Disease') :: ['angle closure glaucoma', 'ACG']
@entity726 :: (MESH:D005901,Disease) :: ['Glaucoma Diagnosis']
