Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

Diagnostic validity of clinical signs associated with a large exophoria at near

Purpose. To analyze the diagnostic validity of accommodative and binocular tests in a sample of patients with a large near exophoria with moderate to severe symptoms. Methods. Two groups of patients between 19 and 35 years were recruited from a university clinic: 33 subjects with large exophoria at near vision and moderate or high visual discomfort and 33 patients with normal heterophoria and low visual discomfort. Visual discomfort was defined using the Conlon survey. A refractive exam and an exhaustive evaluation of accommodation and vergence were assessed. Diagnostic validity by means of receiver operator characteristic (ROC) curves, sensitivity (S), specificity (Sp), and positive and negative likelihood ratios (LR+, LR−) were assessed. This analysis was also carried out considering multiple tests as serial testing strategy. Results. ROC analysis showed the best diagnostic accuracy for receded near point of convergence (NPC) recovery (area = 0.929) and binocular accommodative facility (BAF) (area = 0.886). Using the cut-offs obtained with ROC analysis, the best diagnostic validity was obtained for the combination of NPC recovery and BAF (S  =  0.77, Sp = 1, LR+ = value tending to infinity, LR− = 0.23) and the combination of NPC break and recovery with BAF (S  =  0.73, Sp = 1, LR+ = tending to infinity, LR− = 0.27). Conclusions. NPC and BAF tests were the tests with the best diagnostic accuracy for subjects with large near exophoria and moderate to severe symptoms.